A Multi-Class Classification Model for Text Related to Online Public Opinion Risks in Higher Education Institutions Based on Confidence-Aware Dynamic Fusion

With the widespread use of social media and online platforms in the dissemination of public opinion within universities, the multi-class classification of risk-related texts has become a critical component of online public opinion analysis in higher education institutions. Existing multi-class risk classification methods often focus on static semantic representations, making it difficult to effectively capture the emotional evolution within texts and the differences between samples, which in turn affects the accuracy of risk classification. To address this, this paper proposes a multi-class risk classification model for university online public opinion that integrates contextual semantic modeling, emotional evolution detection, and adaptive confidence-based feature fusion. The model employs pre-trained BERT for context encoding and, while preserving high-level semantic information, enhances the model’s adaptability to domain-specific features through a selective unfreezing strategy. First, a Bidirectional Gated Recurrent Unit (BiGRU) is introduced to model the emotional evolution trajectory within text sequences, and an emotional transition intensity metric is constructed by calculating the difference between adjacent hidden states, thereby explicitly capturing the magnitude of emotional changes. Additionally, a convolutional feature branch is designed to capture local emotional patterns, enhancing the model’s ability to perceive local risk cues and fine-grained emotional fluctuations. Finally, the Emotion-Adaptive Feature Mixer (EAFM) is introduced. This module adaptively weights global emotional evolution features and local emotional pattern features based on sample confidence to adjust the contributions of different feature branches in risk classification. Experimental results demonstrate that the proposed model exhibits good convergence characteristics in the university online public opinion scenario represented by the CUOPO dataset and demonstrates strong interpretability through attention visualization and confidence coefficient analysis.