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戒酒的李白
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README-EN.md
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 <div align="center">
-# 📊 Weibo Public Opinion Multi-Agent Analysis System
-
 <img src="static/image/logo_compressed.png" alt="Weibo Public Opinion Analysis System Logo" width="600">
 [![GitHub Stars](https://img.shields.io/github/stars/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/stargazers)
+[![GitHub Watchers](https://img.shields.io/github/watchers/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/watchers)
 [![GitHub Forks](https://img.shields.io/github/forks/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/network)
 [![GitHub Issues](https://img.shields.io/github/issues/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/issues)
 [![GitHub License](https://img.shields.io/github/license/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/blob/main/LICENSE)
@@ -13,104 +12,57 @@
 </div>
-<div align="center">
-<img src="static/image/banner_compressed.png" alt="banner" width="800">
-</div>
-
 ## 📝 Project Overview
-**Weibo Public Opinion Multi-Agent Analysis System** is an innovative public opinion analysis platform built from scratch, utilizing multi-agent collaborative architecture to provide accurate, real-time, and comprehensive Weibo public opinion monitoring and analysis services. The system achieves full-process automation from data collection and sentiment analysis to report generation through the collaboration of five specialized AI agents.
+**"WeiYu"** is an innovative multi-agent public opinion analysis system built from scratch, featuring universal simplicity across all platforms.
-### 🚀 Key Features
+See the system-generated research report on "Wuhan University Public Opinion":[In-depth Analysis Report on Wuhan University's Brand Reputation](./final_reports/final_report__20250827_131630.html)
-- **Multi-Agent Collaborative Architecture**: 5 specialized agents working together to complete the full process of public opinion analysis
-- **Comprehensive Data Collection**: Integrating Weibo crawlers, news search, multimedia content, and other multi-dimensional data sources
-- **Deep Sentiment Analysis**: Precise multilingual sentiment recognition based on fine-tuned BERT/GPT-2/Qwen models
-- **Intelligent Report Generation**: Automatically generate structured HTML analysis reports with custom template support
-- **Agent Forum Communication**: ForumEngine provides information sharing and collaborative decision-making platform for agents
-- **High-Performance Asynchronous Processing**: Support concurrent processing of multiple public opinion tasks with real-time status monitoring
-- **Cloud Data Support**: Convenient cloud database service with 100,000+ daily real data
+Beyond just report quality, compared to similar products, we have 🚀 six major advantages:
-## 🏗️ System Architecture
+1. **AI-Driven Comprehensive Monitoring**: AI crawler clusters operate 24/7 non-stop, comprehensively covering 10+ key domestic and international social media platforms including Weibo, Xiaohongshu, TikTok, Kuaishou, etc. Not only capturing trending content in real-time, but also drilling down to massive user comments, letting you hear the most authentic and widespread public voice.
-### Overall Architecture Diagram
+2. **Composite Analysis Engine Beyond LLM**: We not only rely on 5 types of professionally designed Agents, but also integrate middleware such as fine-tuned models and statistical models. Through multi-model collaborative work, we ensure the depth, accuracy, and multi-dimensional perspective of analysis results.
-```mermaid
-graph TB
-    subgraph "Frontend Display Layer"
-        UI[Web Interface<br/>Flask + Streamlit]
-    end
-    
-    subgraph "Multi-Agent Collaboration Layer"
-        QE[QueryEngine<br/>News Search Agent]
-        ME[MediaEngine<br/>Multimedia Search Agent]
-        IE[InsightEngine<br/>Deep Insight Agent]
-        RE[ReportEngine<br/>Report Generation Agent]
-        Forum[ForumEngine<br/>Agent Forum Communication Center]
-    end
-    
-    subgraph "Data Processing Layer"
-        MS[MindSpider<br/>Weibo Crawler System]
-        SA[SentimentAnalysis<br/>Sentiment Analysis Model Collection]
-        DB[(MySQL<br/>Database)]
-    end
-    
-    subgraph "External Service Layer"
-        LLM[LLM API<br/>DeepSeek/Kimi/Gemini]
-        Search[Search API<br/>Tavily/Bocha]
-    end
-    
-    UI --> QE
-    UI --> ME
-    UI --> IE
-    UI --> RE
-    
-    QE --> Search
-    ME --> Search
-    IE --> MS
-    IE --> SA
-    
-    QE --> LLM
-    ME --> LLM
-    IE --> LLM
-    RE --> LLM
-    
-    MS --> DB
-    SA --> DB
-    
-    %% Agent Forum Communication Mechanism
-    QE <--> Forum
-    ME <--> Forum
-    IE <--> Forum
-    RE <--> Forum
-```
+3. **Powerful Multimodal Capabilities**: Breaking through text and image limitations, capable of deep analysis of short video content from TikTok, Kuaishou, etc., and precisely extracting structured multimodal information cards such as weather, calendar, stocks from modern search engines, giving you comprehensive control over public opinion dynamics.
-### Agent Collaboration Workflow
+4. **Agent "Forum" Collaboration Mechanism**: Endowing different Agents with unique toolsets and thinking patterns, conducting chain-of-thought collision and debate through the "forum" mechanism. This not only avoids the thinking limitations of single models and homogenization caused by communication, but also catalyzes higher-quality collective intelligence and decision support.
-The system's core workflow is based on multi-agent collaboration:
+5. **Seamless Integration of Public and Private Domain Data**: The platform not only analyzes public opinion, but also provides high-security interfaces supporting seamless integration of your internal business databases with public opinion data. Breaking through data barriers, providing powerful analysis capabilities of "external trends + internal insights" for vertical businesses.
-1. **QueryEngine (News Query Agent)**: Uses Tavily API to search authoritative news reports, providing official information sources
-2. **MediaEngine (Multimedia Search Agent)**: Conducts multimodal content search through Bocha API to gather social media perspectives
-3. **InsightEngine (Deep Insight Agent)**: Queries local Weibo database, combines multiple sentiment analysis models for deep analysis
-4. **ForumEngine (Forum Monitoring Agent)**: Real-time monitoring of agent log outputs, extracts key information and promotes collaboration
-5. **ReportEngine (Report Generation Agent)**: Based on analysis results from all agents, uses Gemini LLM to generate comprehensive HTML reports
+6. **Lightweight and Highly Extensible Framework**: Based on pure Python modular design, achieving lightweight, one-click deployment. Clear code structure allows developers to easily integrate custom models and business logic, enabling rapid platform expansion and deep customization.
-### Project Code Structure
+**Starting with public opinion, but not limited to public opinion**. The goal of "WeiYu" is to become a simple and universal data analysis engine that drives all business scenarios.
+
+<div align="center">
+<img src="static/image/system_schematic.png" alt="banner" width="800">
+
+Say goodbye to traditional data dashboards. In "WeiYu", everything starts with a simple question - you just need to ask your analysis needs like a conversation
+</div>
+
+## 🏗️ System Architecture
+
+### Overall Architecture Diagram
+
+Still drawing...
+
+### Project Code Structure Tree
 ```
 Weibo_PublicOpinion_AnalysisSystem/
-├── QueryEngine/                    # News Query Engine Agent
+├── QueryEngine/                   # Domestic and international news breadth search Agent
 │   ├── agent.py                   # Agent main logic
 │   ├── llms/                      # LLM interface wrapper
 │   ├── nodes/                     # Processing nodes
 │   ├── tools/                     # Search tools
-│   └── utils/                     # Utility functions
-├── MediaEngine/                    # Multimedia Search Engine Agent
+│   ├── utils/                     # Utility functions
+│   └── ...                        # Other modules
+├── MediaEngine/                   # Powerful multimodal understanding Agent
 │   ├── agent.py                   # Agent main logic
 │   ├── llms/                      # LLM interfaces
 │   ├── tools/                     # Search tools
 │   └── ...                        # Other modules
-├── InsightEngine/                 # Data Insight Engine Agent
+├── InsightEngine/                 # Private database mining Agent
 │   ├── agent.py                   # Agent main logic
 │   ├── llms/                      # LLM interface wrapper
 │   │   ├── deepseek.py            # DeepSeek API
@@ -120,7 +72,7 @@ Weibo_PublicOpinion_AnalysisSystem/
 │   ├── nodes/                     # Processing nodes
 │   │   ├── first_search_node.py   # First search node
 │   │   ├── reflection_node.py     # Reflection node
-│   │   ├── summary_nodes.py      # Summary nodes
+│   │   ├── summary_nodes.py       # Summary node
 │   │   ├── search_node.py         # Search node
 │   │   ├── sentiment_node.py      # Sentiment analysis node
 │   │   └── insight_node.py        # Insight generation node
@@ -137,7 +89,7 @@ Weibo_PublicOpinion_AnalysisSystem/
 │       ├── __init__.py
 │       ├── config.py              # Configuration management
 │       └── helpers.py             # Helper functions
-├── ReportEngine/                  # Report Generation Engine Agent
+├── ReportEngine/                  # Multi-round report generation Agent
 │   ├── agent.py                   # Agent main logic
 │   ├── llms/                      # LLM interfaces
 │   │   └── gemini.py              # Gemini API dedicated
@@ -149,9 +101,9 @@ Weibo_PublicOpinion_AnalysisSystem/
 │   │   ├── 商业品牌舆情监测.md
 │   │   └── ...                    # More templates
 │   └── flask_interface.py         # Flask API interface
-├── ForumEngine/                   # Forum Communication Engine Agent
+├── ForumEngine/                   # Forum engine simple implementation
 │   └── monitor.py                 # Log monitoring and forum management
-├── MindSpider/                    # Weibo Crawler System
+├── MindSpider/                    # Weibo crawler system
 │   ├── main.py                    # Crawler main program
 │   ├── BroadTopicExtraction/      # Topic extraction module
 │   │   ├── get_today_news.py      # Today's news fetching
@@ -161,19 +113,21 @@ Weibo_PublicOpinion_AnalysisSystem/
 │   │   └── platform_crawler.py    # Platform crawler management
 │   └── schema/                    # Database schema
 │       └── init_database.py       # Database initialization
-├── SentimentAnalysisModel/        # Sentiment Analysis Model Collection
+├── SentimentAnalysisModel/        # Sentiment analysis model collection
 │   ├── WeiboSentiment_Finetuned/  # Fine-tuned BERT/GPT-2 models
-│   ├── WeiboMultilingualSentiment/ # Multilingual sentiment analysis
-│   ├── WeiboSentiment_SmallQwen/   # Small Qwen model
+│   ├── WeiboMultilingualSentiment/# Multilingual sentiment analysis (recommended)
+│   ├── WeiboSentiment_SmallQwen/  # Small parameter Qwen3 fine-tuning
 │   └── WeiboSentiment_MachineLearning/ # Traditional machine learning methods
-├── SingleEngineApp/               # Individual Agent Streamlit apps
+├── SingleEngineApp/               # Individual Agent Streamlit applications
 │   ├── query_engine_streamlit_app.py
 │   ├── media_engine_streamlit_app.py
 │   └── insight_engine_streamlit_app.py
 ├── templates/                     # Flask templates
-│   └── index.html                # Main interface template
+│   └── index.html                 # Main interface frontend
 ├── static/                        # Static resources
 ├── logs/                          # Runtime log directory
+├── final_reports/                 # Final generated HTML report files
+├── utils/                         # Common utility functions
 ├── app.py                         # Flask main application entry
 ├── config.py                      # Global configuration file
 └── requirements.txt               # Python dependency list
@@ -183,26 +137,27 @@ Weibo_PublicOpinion_AnalysisSystem/
 ### System Requirements
-- **Operating System**: Windows 10/11 (Linux/macOS also supported)
-- **Python Version**: 3.11+
+- **Operating System**: Windows, Linux, MacOS
+- **Python Version**: 3.9+
 - **Conda**: Anaconda or Miniconda
-- **Database**: MySQL 8.0+ (or choose our cloud database service)
-- **Memory**: 8GB+ recommended
+- **Database**: MySQL (optional, you can choose our cloud database service)
+- **Memory**: 2GB+ recommended
 ### 1. Create Conda Environment
 ```bash
-# Create conda environment named pytorch_python11
-conda create -n pytorch_python11 python=3.11
-conda activate pytorch_python11
+# Create conda environment
+conda create -n your_conda_name python=3.11
+conda activate your_conda_name
 ```
 ### 2. Install Dependencies
 ```bash
-# Install basic dependencies
+# Basic dependency installation
 pip install -r requirements.txt
+#========Below are optional========
 # If you need local sentiment analysis functionality, install PyTorch
 # CPU version
 pip install torch torchvision torchaudio
@@ -225,7 +180,7 @@ playwright install chromium
 #### 4.1 Configure API Keys
-Edit the `config.py` file and fill in your API keys:
+Edit the `config.py` file and fill in your API keys (you can also choose your own models and search proxies):
 ```python
 # MySQL Database Configuration
@@ -266,10 +221,9 @@ python schema/init_database.py
 **Option 2: Use Cloud Database Service (Recommended)**
-We provide convenient cloud database service with 100,000+ daily real Weibo data, currently **free application** during the promotion period!
+We provide convenient cloud database service with 100,000+ daily real public opinion data, currently **free application** during the promotion period!
-- Real Weibo data, updated in real-time
-- Pre-processed sentiment annotation data
+- Real public opinion data, updated in real-time
 - Multi-dimensional tag classification
 - High-availability cloud service
 - Professional technical support
@@ -282,12 +236,14 @@ We provide convenient cloud database service with 100,000+ daily real Weibo data
 ```bash
 # In project root directory, activate conda environment
-conda activate pytorch_python11
+conda activate your_conda_name
-# Start main application (automatically starts all agents)
+# Start main application
 python app.py
 ```
+> Note: Data crawling requires separate operation, see section 5.3 for guidance
+
 Visit http://localhost:5000 to use the complete system
 #### 5.2 Launch Individual Agents
@@ -303,7 +259,9 @@ streamlit run SingleEngineApp/media_engine_streamlit_app.py --server.port 8502
 streamlit run SingleEngineApp/insight_engine_streamlit_app.py --server.port 8501
 ```
-#### 5.3 Standalone Crawler System
+#### 5.3 Crawler System Standalone Use
+
+This section has detailed configuration documentation: [MindSpider Usage Guide](./MindSpider/README.md)
 ```bash
 # Enter crawler directory
@@ -322,58 +280,6 @@ python main.py --broad-topic --date 2024-01-20
 python main.py --deep-sentiment --platforms xhs dy wb
 ```
-## 💾 Database Configuration
-
-### Local Database Configuration
-
-1. **Install MySQL 8.0+**
-2. **Create Database**:
-   ```sql
-   CREATE DATABASE weibo_analysis CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
-   ```
-3. **Run Initialization Script**:
-   ```bash
-   cd MindSpider
-   python schema/init_database.py
-   ```
-
-### Auto-Crawling Configuration
-
-Configure automatic crawling tasks for continuous data updates:
-
-```python
-# Configure crawler parameters in MindSpider/config.py
-CRAWLER_CONFIG = {
-    'max_pages': 200,         # Maximum pages to crawl
-    'delay': 1,               # Request delay (seconds)
-    'timeout': 30,            # Timeout (seconds)
-    'platforms': ['xhs', 'dy', 'wb', 'bili'],  # Crawling platforms
-    'daily_keywords': 100,    # Daily keywords count
-    'max_notes_per_keyword': 50,  # Max content per keyword
-    'use_proxy': False,       # Whether to use proxy
-}
-```
-
-### Cloud Database Service (Recommended)
-
-**Why Choose Our Cloud Database Service?**
-
-- **Rich Data Sources**: 100,000+ daily real Weibo data covering hot topics across all industries
-- **High-Quality Annotations**: Professional team manually annotated sentiment data with 95%+ accuracy
-- **Multi-Dimensional Analysis**: Including topic classification, sentiment tendency, influence scoring and other multi-dimensional tags
-- **Real-Time Updates**: 24/7 continuous data collection ensuring timeliness
-- **Technical Support**: Professional team providing technical support and customization services
-
-**Application Method**:
-📧 Email Contact: 670939375@qq.com
-📝 Email Subject: Apply for Weibo Public Opinion Cloud Database Access
-📝 Email Content: Please describe your use case and expected data volume requirements
-
-**Promotion Period Benefits**:
-- Free basic cloud database access
-- Free technical support and deployment guidance
-- Priority access to new features
-
 ## ⚙️ Advanced Configuration
 ### Modify Key Parameters
@@ -420,7 +326,7 @@ The system supports multiple LLM providers, switchable in each agent's configura
 ```python
 # Configure in each Engine's utils/config.py
 class Config:
-    default_llm_provider = "deepseek"  # Options: "deepseek", "openai", "kimi", "gemini"
+    default_llm_provider = "deepseek"  # Options: "deepseek", "openai", "kimi", "gemini", "qwen"
     # DeepSeek configuration
     deepseek_api_key = "your_api_key"
@@ -444,40 +350,40 @@ class Config:
 The system integrates multiple sentiment analysis methods, selectable based on needs:
-#### 1. BERT-based Fine-tuned Model (Highest Accuracy)
+#### 1. Multilingual Sentiment Analysis
 ```bash
-# Use BERT Chinese model
-cd SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora
-python predict.py --text "This product is really great"
+cd SentimentAnalysisModel/WeiboMultilingualSentiment
+python predict.py --text "This product is amazing!" --lang "en"
 ```
-#### 2. GPT-2 LoRA Fine-tuned Model (Faster Speed)
+#### 2. Small Parameter Qwen3 Fine-tuning
 ```bash
-cd SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora
-python predict.py --text "I'm not feeling great today"
+cd SentimentAnalysisModel/WeiboSentiment_SmallQwen
+python predict_universal.py --text "This event was very successful"
 ```
-#### 3. Small Qwen Model (Balanced)
+#### 3. BERT-based Fine-tuned Model
 ```bash
-cd SentimentAnalysisModel/WeiboSentiment_SmallQwen
-python predict_universal.py --text "This event was very successful"
+# Use BERT Chinese model
+cd SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora
+python predict.py --text "This product is really great"
 ```
-#### 4. Traditional Machine Learning Methods (Lightweight)
+#### 4. GPT-2 LoRA Fine-tuned Model
 ```bash
-cd SentimentAnalysisModel/WeiboSentiment_MachineLearning
-python predict.py --model_type "svm" --text "Service attitude needs improvement"
+cd SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora
+python predict.py --text "I'm not feeling great today"
 ```
-#### 5. Multilingual Sentiment Analysis (Supports 22 Languages)
+#### 5. Traditional Machine Learning Methods
 ```bash
-cd SentimentAnalysisModel/WeiboMultilingualSentiment
-python predict.py --text "This product is amazing!" --lang "en"
+cd SentimentAnalysisModel/WeiboSentiment_MachineLearning
+python predict.py --model_type "svm" --text "Service attitude needs improvement"
 ```
 ### Integrate Custom Business Database
@@ -538,45 +444,13 @@ class DeepSearchAgent:
 ### Custom Report Templates
-#### 1. Create Template Files
-
-Create new Markdown templates in the `ReportEngine/report_template/` directory:
-
-```markdown
-<!-- Enterprise Brand Monitoring Report.md -->
-# Enterprise Brand Public Opinion Monitoring Report
-
-## 📊 Executive Summary
-{executive_summary}
-
-## 🔍 Brand Mention Analysis
-### Mention Volume Trends
-{mention_trend}
-
-### Sentiment Distribution
-{sentiment_distribution}
-
-## 📈 Competitor Analysis
-{competitor_analysis}
-
-## 🎯 Key Insights Summary
-{key_insights}
+#### 1. Upload in Web Interface
-## ⚠️ Risk Alerts
-{risk_alerts}
+The system supports uploading custom template files (.md or .txt format), selectable when generating reports.
-## 📋 Improvement Recommendations
-{recommendations}
+#### 2. Create Template Files
----
-*Report Type: Enterprise Brand Public Opinion Monitoring*  
-*Generation Time: {generation_time}*  
-*Data Sources: {data_sources}*
-```
-
-#### 2. Use in Web Interface
-
-The system supports uploading custom template files (.md or .txt format), selectable when generating reports.
+Create new templates in the `ReportEngine/report_template/` directory, and our Agent will automatically select the most appropriate template.
 ## 🤝 Contributing Guide
@@ -590,15 +464,6 @@ We welcome all forms of contributions!
 4. **Push to branch**: `git push origin feature/AmazingFeature`
 5. **Open Pull Request**
-### Contribution Types
-
-- 🐛 Bug fixes
-- ✨ New feature development
-- 📚 Documentation improvements
-- 🎨 UI/UX improvements
-- ⚡ Performance optimization
-- 🧪 Test case additions
-
 ### Development Standards
 - Code follows PEP8 standards
@@ -608,7 +473,7 @@ We welcome all forms of contributions!
 ## 📄 License
-This project is licensed under the [MIT License](LICENSE). Please see the LICENSE file for details.
+This project is licensed under the [GPL-2.0 License](LICENSE). Please see the LICENSE file for details.
 ## 🎉 Support & Contact
@@ -621,8 +486,6 @@ This project is licensed under the [MIT License](LICENSE). Please see the LICENS
 ### Contact Information
 - 📧 **Email**: 670939375@qq.com
-- 💬 **QQ Group**: [Join Technical Discussion Group]
-- 🐦 **WeChat**: [Scan QR Code for Technical Support]
 ### Business Cooperation
@@ -635,7 +498,7 @@ This project is licensed under the [MIT License](LICENSE). Please see the LICENS
 **Free Cloud Database Service Application**:
 📧 Send email to: 670939375@qq.com  
-📝 Subject: Weibo Public Opinion Cloud Database Application  
+📝 Subject: WeiYu Cloud Database Application  
 📝 Description: Your use case and requirements  
 ## 👥 Contributors
@@ -650,6 +513,4 @@ Thanks to these excellent contributors:
 **⭐ If this project helps you, please give us a star!**
-Made with ❤️ by [Weibo Public Opinion Analysis Team](https://github.com/666ghj)
-
 </div>
--- a/README.md
View file @732de24
+++ b/README.md
View file @732de24
@@ -2,9 +2,8 @@
 <img src="static/image/logo_compressed.png" alt="Weibo Public Opinion Analysis System Logo" width="600">
-# 微舆 - 致力于打造简洁通用的舆情分析平台
-
 [![GitHub Stars](https://img.shields.io/github/stars/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/stargazers)
+[![GitHub Watchers](https://img.shields.io/github/watchers/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/watchers)
 [![GitHub Forks](https://img.shields.io/github/forks/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/network)
 [![GitHub Issues](https://img.shields.io/github/issues/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/issues)
 [![GitHub License](https://img.shields.io/github/license/666ghj/Weibo_PublicOpinion_AnalysisSystem?style=flat-square)](https://github.com/666ghj/Weibo_PublicOpinion_AnalysisSystem/blob/main/LICENSE)
@@ -13,104 +12,57 @@
 </div>
-<div align="center">
-<img src="static/image/system_schematic.png" alt="banner" width="800">
-</div>
-
 ## 📝 项目概述
-**微博舆情分析多智能体系统**是一个从零构建的创新型舆情分析平台，采用多Agent协作架构，致力于提供准确、实时、全面的微博舆情监测与分析服务。系统通过五个专门化的AI Agent协同工作，实现了从数据采集、情感分析到报告生成的全流程自动化。
+“**微舆**” 是一个从0实现的创新型 多智能体 舆情分析系统，全平台简洁通用。
-### 🚀 核心亮点
+查看系统以“武汉大学舆情”为例，生成的研究报告：[武汉大学品牌声誉深度分析报告](./final_reports/final_report__20250827_131630.html)
-- **多智能体协作架构**：5个专门化Agent各司其职，协同工作完成舆情分析全流程
-- **全方位数据采集**：整合微博爬虫、新闻搜索、多媒体内容等多维度数据源
-- **深度情感分析**：基于微调BERT/GPT-2/Qwen模型的精准多语言情感识别
-- **智能报告生成**：自动生成结构化HTML分析报告，支持自定义模板
-- **Agent论坛交流**：ForumEngine提供Agent间信息共享和协作决策平台
-- **高性能异步处理**：支持并发处理多个舆情任务，实时状态监控
-- **云端数据支持**：提供便捷云数据库服务，日均10万+真实数据
+不仅仅体现在报告质量上，相比同类产品，我们拥有🚀六大优势：
-## 🏗️ 系统架构
+1. **AI驱动的全域监控**：AI爬虫集群7x24小时不间断作业，全面覆盖微博、小红书、抖音、快手等10+国内外关键社媒。不仅实时捕获热点内容，更能下钻至海量用户评论，让您听到最真实、最广泛的大众声音。
-### 整体架构图
+2. **超越LLM的复合分析引擎**：我们不仅依赖设计的5类专业Agent，更融合了微调模型、统计模型等中间件。通过多模型协同工作，确保了分析结果的深度、准度与多维视角。
-```mermaid
-graph TB
-    subgraph "前端展示层"
-        UI[Web界面<br/>Flask + Streamlit]
-    end
-    
-    subgraph "多Agent协作层"
-        QE[QueryEngine<br/>新闻搜索Agent]
-        ME[MediaEngine<br/>多媒体搜索Agent]
-        IE[InsightEngine<br/>深度洞察Agent]
-        RE[ReportEngine<br/>报告生成Agent]
-        Forum[ForumEngine<br/>Agent论坛交流中心]
-    end
-    
-    subgraph "数据处理层"
-        MS[MindSpider<br/>微博爬虫系统]
-        SA[SentimentAnalysis<br/>情感分析模型集合]
-        DB[(MySQL<br/>数据库)]
-    end
-    
-    subgraph "外部服务层"
-        LLM[LLM API<br/>DeepSeek/Kimi/Gemini]
-        Search[搜索API<br/>Tavily/Bocha]
-    end
-    
-    UI --> QE
-    UI --> ME
-    UI --> IE
-    UI --> RE
-    
-    QE --> Search
-    ME --> Search
-    IE --> MS
-    IE --> SA
-    
-    QE --> LLM
-    ME --> LLM
-    IE --> LLM
-    RE --> LLM
-    
-    MS --> DB
-    SA --> DB
-    
-    %% Agent论坛交流机制
-    QE <--> Forum
-    ME <--> Forum
-    IE <--> Forum
-    RE <--> Forum
-```
+3. **强大的多模态能力**：突破图文限制，能深度解析抖音、快手等短视频内容，并精准提取现代搜索引擎中的天气、日历、股票等结构化多模态信息卡片，让您全面掌握舆情动态。
+
+4. **Agent“论坛”协作机制**：为不同Agent赋予独特的工具集与思维模式，通过“论坛”机制进行链式思维碰撞与辩论。这不仅避免了单一模型的思维局限与交流导致的同质化，更催生出更高质量的集体智能与决策支持。
+
+5. **公私域数据无缝融合**：平台不仅分析公开舆情，还提供高安全性的接口，支持您将内部业务数据库与舆情数据无缝集成。打通数据壁垒，为垂直业务提供“外部趋势+内部洞察”的强大分析能力。
-### Agent协作流程
+6. **轻量化与高扩展性框架**：基于纯Python模块化设计，实现轻量化、一键式部署。代码结构清晰，开发者可轻松集成自定义模型与业务逻辑，实现平台的快速扩展与深度定制。
-系统核心工作流程基于多Agent协作模式：
+**始于舆情，而不止于舆情**。“微舆”的目标，是成为驱动一切业务场景的简洁通用的数据分析引擎。
+
+<div align="center">
+<img src="static/image/system_schematic.png" alt="banner" width="800">
+
+告别传统的数据看板，在“微舆”，一切由一个简单的问题开始，您只需像对话一样，提出您的分析需求
+</div>
+
+## 🏗️ 系统架构
+
+### 整体架构图
-1. **QueryEngine（新闻查询Agent）**：使用Tavily API搜索权威新闻报道，提供官方信息源
-2. **MediaEngine（多媒体搜索Agent）**：通过Bocha API进行多模态内容搜索，获取社交媒体观点
-3. **InsightEngine（深度洞察Agent）**：查询本地微博数据库，结合多种情感分析模型进行深度分析
-4. **ForumEngine（论坛监控Agent）**：实时监控各Agent日志输出，提取关键信息并促进协作
-5. **ReportEngine（报告生成Agent）**：基于所有Agent的分析结果，使用Gemini LLM生成综合HTML报告
+还在画...
-### 项目代码结构
+### 项目代码结构树
 ```
 Weibo_PublicOpinion_AnalysisSystem/
-├── QueryEngine/                    # 新闻查询引擎Agent
+├── QueryEngine/                   # 国内外新闻广度搜索Agent
 │   ├── agent.py                   # Agent主逻辑
 │   ├── llms/                      # LLM接口封装
 │   ├── nodes/                     # 处理节点
 │   ├── tools/                     # 搜索工具
-│   └── utils/                     # 工具函数
-├── MediaEngine/                    # 多媒体搜索引擎Agent
+│   ├── utils/                     # 工具函数
+│   └── ...                        # 其他模块
+├── MediaEngine/                   # 强大的多模态理解Agent
 │   ├── agent.py                   # Agent主逻辑
 │   ├── llms/                      # LLM接口
 │   ├── tools/                     # 搜索工具
 │   └── ...                        # 其他模块
-├── InsightEngine/                 # 数据洞察引擎Agent
+├── InsightEngine/                 # 私有数据库挖掘Agent
 │   ├── agent.py                   # Agent主逻辑
 │   ├── llms/                      # LLM接口封装
 │   │   ├── deepseek.py            # DeepSeek API
@@ -137,7 +89,7 @@ Weibo_PublicOpinion_AnalysisSystem/
 │       ├── __init__.py
 │       ├── config.py              # 配置管理
 │       └── helpers.py             # 辅助函数
-├── ReportEngine/                  # 报告生成引擎Agent
+├── ReportEngine/                  # 多轮报告生成Agent
 │   ├── agent.py                   # Agent主逻辑
 │   ├── llms/                      # LLM接口
 │   │   └── gemini.py              # Gemini API专用
@@ -149,31 +101,33 @@ Weibo_PublicOpinion_AnalysisSystem/
 │   │   ├── 商业品牌舆情监测.md
 │   │   └── ...                    # 更多模板
 │   └── flask_interface.py         # Flask API接口
-├── ForumEngine/                   # 论坛交流引擎Agent
+├── ForumEngine/                   # 论坛引擎简易实现
 │   └── monitor.py                 # 日志监控和论坛管理
 ├── MindSpider/                    # 微博爬虫系统
 │   ├── main.py                    # 爬虫主程序
 │   ├── BroadTopicExtraction/      # 话题提取模块
 │   │   ├── get_today_news.py      # 今日新闻获取
 │   │   └── topic_extractor.py     # 话题提取器
-│   ├── DeepSentimentCrawling/     # 深度情感爬取
+│   ├── DeepSentimentCrawling/     # 深度舆情爬取
 │   │   ├── MediaCrawler/          # 媒体爬虫核心
 │   │   └── platform_crawler.py    # 平台爬虫管理
 │   └── schema/                    # 数据库结构
 │       └── init_database.py       # 数据库初始化
 ├── SentimentAnalysisModel/        # 情感分析模型集合
 │   ├── WeiboSentiment_Finetuned/  # 微调BERT/GPT-2模型
-│   ├── WeiboMultilingualSentiment/ # 多语言情感分析
-│   ├── WeiboSentiment_SmallQwen/   # 小型Qwen模型
+│   ├── WeiboMultilingualSentiment/# 多语言情感分析（推荐）
+│   ├── WeiboSentiment_SmallQwen/  # 小参数Qwen3微调
 │   └── WeiboSentiment_MachineLearning/ # 传统机器学习方法
 ├── SingleEngineApp/               # 单独Agent的Streamlit应用
 │   ├── query_engine_streamlit_app.py
 │   ├── media_engine_streamlit_app.py
 │   └── insight_engine_streamlit_app.py
 ├── templates/                     # Flask模板
-│   └── index.html                # 主界面模板
+│   └── index.html                 # 主界面前端
 ├── static/                        # 静态资源
 ├── logs/                          # 运行日志目录
+├── final_reports/                 # 最终生成的HTML报告文件
+├── utils/                         # 通用工具函数
 ├── app.py                         # Flask主应用入口
 ├── config.py                      # 全局配置文件
 └── requirements.txt               # Python依赖包清单
@@ -183,18 +137,18 @@ Weibo_PublicOpinion_AnalysisSystem/
 ### 环境要求
-- **操作系统**: Windows 10/11（Linux/macOS也支持）
-- **Python版本**: 3.11+
+- **操作系统**: Windows、Linux、MacOS
+- **Python版本**: 3.9+
 - **Conda**: Anaconda或Miniconda
-- **数据库**: MySQL 8.0+（可选择我们的云数据库服务）
-- **内存**: 建议8GB以上
+- **数据库**: MySQL（可选择我们的云数据库服务）
+- **内存**: 建议2GB以上
 ### 1. 创建Conda环境
 ```bash
-# 创建名为pytorch_python11的conda环境
-conda create -n pytorch_python11 python=3.11
-conda activate pytorch_python11
+# 创建conda环境
+conda create -n your_conda_name python=3.11
+conda activate your_conda_name
 ```
 ### 2. 安装依赖包
@@ -203,6 +157,7 @@ conda activate pytorch_python11
 # 基础依赖安装
 pip install -r requirements.txt
+#========下面是可选项========
 # 如果需要本地情感分析功能，安装PyTorch
 # CPU版本
 pip install torch torchvision torchaudio
@@ -225,7 +180,7 @@ playwright install chromium
 #### 4.1 配置API密钥
-编辑 `config.py` 文件，填入您的API密钥：
+编辑 `config.py` 文件，填入您的API密钥（您也可以选择自己的模型、搜索代理）：
 ```python
 # MySQL数据库配置
@@ -266,10 +221,9 @@ python schema/init_database.py
 **选择2：使用云数据库服务（推荐）**
-我们提供便捷的云数据库服务，包含日均10万+真实微博数据，目前推广期间**免费申请**！
+我们提供便捷的云数据库服务，包含日均10万+真实舆情数据，目前推广期间**免费申请**！
-- 真实微博数据，实时更新
-- 预处理的情感标注数据
+- 真实舆情数据，实时更新
 - 多维度标签分类
 - 高可用云端服务
 - 专业技术支持
@@ -282,12 +236,14 @@ python schema/init_database.py
 ```bash
 # 在项目根目录下，激活conda环境
-conda activate pytorch_python11
+conda activate your_conda_name
-# 启动主应用（自动启动所有Agent）
+# 启动主应用即可
 python app.py
 ```
+> 注：数据爬取需要单独操作，见5.3指引
+
 访问 http://localhost:5000 即可使用完整系统
 #### 5.2 单独启动某个Agent
@@ -305,6 +261,8 @@ streamlit run SingleEngineApp/insight_engine_streamlit_app.py --server.port 8501
 #### 5.3 爬虫系统单独使用
+这部分有详细的配置文档：[MindeSpider使用说明](./MindSpider/README.md)
+
 ```bash
 # 进入爬虫目录
 cd MindSpider
@@ -322,65 +280,13 @@ python main.py --broad-topic --date 2024-01-20
 python main.py --deep-sentiment --platforms xhs dy wb
 ```
-## 💾 数据库配置
-
-### 本地数据库配置
-
-1. **安装MySQL 8.0+**
-2. **创建数据库**：
-   ```sql
-   CREATE DATABASE weibo_analysis CHARACTER SET utf8mb4 COLLATE utf8mb4_unicode_ci;
-   ```
-3. **运行初始化脚本**：
-   ```bash
-   cd MindSpider
-   python schema/init_database.py
-   ```
-
-### 自动爬取配置
-
-配置自动爬取任务，实现数据的持续更新：
-
-```python
-# MindSpider/config.py 中配置爬虫参数
-CRAWLER_CONFIG = {
-    'max_pages': 200,         # 最大爬取页数
-    'delay': 1,               # 请求延迟（秒）
-    'timeout': 30,            # 超时时间（秒）
-    'platforms': ['xhs', 'dy', 'wb', 'bili'],  # 爬取平台
-    'daily_keywords': 100,    # 每日关键词数量
-    'max_notes_per_keyword': 50,  # 每关键词最大内容数
-    'use_proxy': False,       # 是否使用代理
-}
-```
-
-### 云数据库服务（推荐）
-
-**为什么选择我们的云数据库服务？**
-
-- **丰富数据源**：日均10万+真实微博数据，涵盖各行业热点话题
-- **高质量标注**：专业团队人工标注的情感数据，准确率95%+
-- **多维度分析**：包含话题分类、情感倾向、影响力评分等多维标签
-- **实时更新**：24小时不间断数据采集，确保时效性
-- **技术支持**：专业团队提供技术支持和定制化服务
-
-**申请方式**：
-📧 邮件联系：670939375@qq.com
-📝 邮件标题：申请微博舆情云数据库访问
-📝 邮件内容：请说明您的使用场景和预期数据量需求
-
-**推广期福利**：
-- 免费提供基础版云数据库访问
-- 免费技术支持和部署指导
-- 优先体验新功能特性
-
 ## ⚙️ 高级配置
 ### 修改关键参数
 #### Agent配置参数
-每个Agent都有专门的配置文件，可根据需求调整：
+每个Agent都有专门的配置文件，可根据需求调整，下面是部分示例：
 ```python
 # QueryEngine/utils/config.py
@@ -406,7 +312,7 @@ class Config:
 ```python
 # InsightEngine/tools/sentiment_analyzer.py
 SENTIMENT_CONFIG = {
-    'model_type': 'multilingual',     # 可选: 'bert', 'multilingual', 'qwen'
+    'model_type': 'multilingual',     # 可选: 'bert', 'multilingual', 'qwen'等
     'confidence_threshold': 0.8,      # 置信度阈值
     'batch_size': 32,                 # 批处理大小
     'max_sequence_length': 512,       # 最大序列长度
@@ -420,7 +326,7 @@ SENTIMENT_CONFIG = {
 ```python
 # 在各Engine的utils/config.py中配置
 class Config:
-    default_llm_provider = "deepseek"  # 可选: "deepseek", "openai", "kimi", "gemini"
+    default_llm_provider = "deepseek"  # 可选: "deepseek", "openai", "kimi", "gemini"，"qwen"等
     # DeepSeek配置
     deepseek_api_key = "your_api_key"
@@ -444,40 +350,40 @@ class Config:
 系统集成了多种情感分析方法，可根据需求选择：
-#### 1. 基于BERT的微调模型（精度最高）
+#### 1. 多语言情感分析
 ```bash
-# 使用BERT中文模型
-cd SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora
-python predict.py --text "这个产品真的很不错"
+cd SentimentAnalysisModel/WeiboMultilingualSentiment
+python predict.py --text "This product is amazing!" --lang "en"
 ```
-#### 2. GPT-2 LoRA微调模型（速度较快）
+#### 2. 小参数Qwen3微调
 ```bash
-cd SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora
-python predict.py --text "今天心情不太好"
+cd SentimentAnalysisModel/WeiboSentiment_SmallQwen
+python predict_universal.py --text "这次活动办得很成功"
 ```
-#### 3. 小型Qwen模型（平衡型）
+#### 3. 基于BERT的微调模型
 ```bash
-cd SentimentAnalysisModel/WeiboSentiment_SmallQwen
-python predict_universal.py --text "这次活动办得很成功"
+# 使用BERT中文模型
+cd SentimentAnalysisModel/WeiboSentiment_Finetuned/BertChinese-Lora
+python predict.py --text "这个产品真的很不错"
 ```
-#### 4. 传统机器学习方法（轻量级）
+#### 4. GPT-2 LoRA微调模型
 ```bash
-cd SentimentAnalysisModel/WeiboSentiment_MachineLearning
-python predict.py --model_type "svm" --text "服务态度需要改进"
+cd SentimentAnalysisModel/WeiboSentiment_Finetuned/GPT2-Lora
+python predict.py --text "今天心情不太好"
 ```
-#### 5. 多语言情感分析（支持22种语言）
+#### 5. 传统机器学习方法
 ```bash
-cd SentimentAnalysisModel/WeiboMultilingualSentiment
-python predict.py --text "This product is amazing!" --lang "en"
+cd SentimentAnalysisModel/WeiboSentiment_MachineLearning
+python predict.py --model_type "svm" --text "服务态度需要改进"
 ```
 ### 接入自定义业务数据库
@@ -538,45 +444,13 @@ class DeepSearchAgent:
 ### 自定义报告模板
-#### 1. 创建模板文件
-
-在 `ReportEngine/report_template/` 目录下创建新的Markdown模板：
-
-```markdown
-<!-- 企业品牌监测报告.md -->
-# 企业品牌舆情监测报告
-
-## 📊 执行摘要
-{executive_summary}
-
-## 🔍 品牌提及分析
-### 提及量趋势
-{mention_trend}
-
-### 情感分布
-{sentiment_distribution}
-
-## 📈 竞品对比分析
-{competitor_analysis}
-
-## 🎯 关键观点摘要
-{key_insights}
+#### 1. 在Web界面中上传
-## ⚠️ 风险预警
-{risk_alerts}
+系统支持上传自定义模板文件（.md或.txt格式），可在生成报告时选择使用。
-## 📋 改进建议
-{recommendations}
+#### 2. 创建模板文件
----
-*报告类型：企业品牌舆情监测*  
-*生成时间：{generation_time}*  
-*数据来源：{data_sources}*
-```
-
-#### 2. 在Web界面中使用
-
-系统支持上传自定义模板文件（.md或.txt格式），可在生成报告时选择使用。
+在 `ReportEngine/report_template/` 目录下创建新的模板，我们的Agent会自行选用最合适的模板。
 ## 🤝 贡献指南
@@ -590,15 +464,6 @@ class DeepSearchAgent:
 4. **推送到分支**：`git push origin feature/AmazingFeature`
 5. **开启Pull Request**
-### 贡献类型
-
-- 🐛 Bug修复
-- ✨ 新功能开发
-- 📚 文档完善
-- 🎨 UI/UX改进
-- ⚡ 性能优化
-- 🧪 测试用例添加
-
 ### 开发规范
 - 代码遵循PEP8规范
@@ -608,7 +473,7 @@ class DeepSearchAgent:
 ## 📄 许可证
-本项目采用 [MIT许可证](LICENSE)。详细信息请参阅LICENSE文件。
+本项目采用 [GPL-2.0许可证](LICENSE)。详细信息请参阅LICENSE文件。
 ## 🎉 支持与联系
@@ -621,8 +486,6 @@ class DeepSearchAgent:
 ### 联系方式
 - 📧 **邮箱**：670939375@qq.com
-- 💬 **QQ群**：[加入技术交流群]
-- 🐦 **微信**：[扫码添加技术支持]
 ### 商务合作
@@ -635,7 +498,7 @@ class DeepSearchAgent:
 **免费云数据库服务申请**：
 📧 发送邮件至：670939375@qq.com  
-📝 标题：微博舆情云数据库申请  
+📝 标题：微舆云数据库申请  
 📝 说明：您的使用场景和需求
 ## 👥 贡献者
@@ -650,6 +513,4 @@ class DeepSearchAgent:
 **⭐ 如果这个项目对您有帮助，请给我们一个星标！**
-Made with ❤️ by [微博舆情分析团队](https://github.com/666ghj)
-
 </div>