Enterprise AI Analysis
Deep Learning and Public Opinion Guidance in Ideological Education: Model Design and Application Exploration
Authors: Honglin Li, Jingchen Zang, Wenze Li
Publication: IECA 2026, January 16-18, 2026, Shanghai, China
With the rapid development of the Internet and social media, ideological education in universities faces challenges from complex and volatile online public opinion environments. This study proposes a deep learning-based ideological education public opinion guidance model (DL-IEPOG), aimed at real-time monitoring, analysis, and guidance of university network public opinion situations. The model employs an improved BERT-BILSTM-Attention architecture for public opinion sentiment analysis, combines Graph Convolutional Networks (GCN) to mine opinion propagation paths, and optimizes guidance schemes through reinforcement learning strategies. Experimental validation on a dataset containing 7,843 real university public opinion data shows that the model achieves 94.267% accuracy in sentiment classification tasks, 91.538% F1 score for public opinion early warning, and 88.921 points for guidance effectiveness evaluation. Experimental results demonstrate that the model can effectively identify public opinion risk points in ideological education, provide scientific guidance strategies for educators, and significantly improve the pertinence and effectiveness of ideological education. This study provides new ideas and methods for the application of deep learning technology in the field of ideological education.
Executive Impact: Key Findings at a Glance
The DL-IEPOG model significantly enhances ideological education by providing real-time public opinion monitoring, sentiment analysis, propagation path mining, and optimized guidance strategies. It achieves high accuracy in sentiment classification and improves guidance effectiveness, offering scientific decision support for educators.
94.267%
Sentiment Accuracy
91.538%
Early Warning F1 Score
88.921 points
Guidance Effectiveness
Deep Analysis & Enterprise Applications
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Sentiment Analysis
Propagation Mechanism Analysis
Guidance Strategy Optimization
The model uses an improved BERT-BiLSTM-Attention network for precise sentiment recognition, achieving high accuracy and F1 scores. It introduces a 'sensitive' category crucial for early risk detection in ideological education.
Graph Convolutional Networks (GCN) are employed to map opinion propagation, identify key nodes, and analyze influence. This provides crucial insights into how ideological issues spread online.
Reinforcement Learning (Deep Q-Network) generates optimal guidance strategies based on real-time public opinion dynamics and historical data, significantly enhancing the effectiveness of interventions.
94.267%
Sentiment Classification Accuracy
DL-IEPOG Model Workflow
Data Collection
→
Preprocessing
→
Sentiment Analysis (BERT-BiLSTM-Attention)
→
Sentiment Classification
→
Graph Construction
→
GCN Analysis
→
Key Node Identification
→
Guidance Decision (Deep Q-Network)
→
Strategy Optimization
| Model | Accuracy (%) | F1 Score (%) | Key Features |
|---|---|---|---|
| DL-IEPOG (Ours) | 94.267 | 93.125 |
|
| BERT | 91.453 | 89.918 |
|
| Traditional ML (SVM/RF) | 81.367 | 79.885 |
|
Case Study: Real-time Public Opinion Guidance
Scenario: A university detects a sensitive online discussion about a campus safety incident.
Solution: DL-IEPOG model immediately classifies sentiment as 'sensitive' (within 2 hours), identifies key influencers through GCN, and recommends targeted communication and phased refutation via Reinforcement Learning.
Outcome: Negative sentiment conversion improved by 41.2%, calming time reduced from 72.3h to 34.6h, resulting in effective crisis management and positive guidance.
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Your AI Implementation Roadmap
A phased approach to integrate deep learning for public opinion guidance into your organization, from data foundation to ongoing refinement.
Phase 1: Data Integration & Model Setup (1-2 Months)
Establish secure data pipelines for university network platforms, preprocess historical public opinion data, and deploy the foundational BERT-BiLSTM-Attention sentiment analysis model. Fine-tune for domain-specific terminology.
Phase 2: GCN Integration & Propagation Mapping (2-3 Months)
Integrate Graph Convolutional Networks (GCN) to analyze opinion propagation. Develop real-time graph construction and key node identification modules. Begin initial testing of propagation analysis capabilities.
Phase 3: Reinforcement Learning & Strategy Optimization (3-4 Months)
Implement the Deep Q-Network for guidance strategy optimization. Train the RL agent using simulated public opinion scenarios and historical guidance data. Develop a user interface for educators to interact with recommended strategies.
Phase 4: Pilot Deployment & Iterative Refinement (Ongoing)
Pilot the DL-IEPOG system within a university department. Collect feedback, monitor performance, and iteratively refine model parameters and guidance algorithms based on real-world application. Expand to broader campus deployment.
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