Major health emergencies often trigger rapid changes in public risk perception, amplified by the widespread dissemination of information through intelligent networks. Accurately capturing these dynamics and assessing the authenticity of online information is crucial for effective crisis management. This research proposes a data-driven framework to analyze the dynamic evolution of public risk perception and the reliability of information dissemination during viral disease emergencies. The approach integrates association rule mining with an Artificial Lizard Search-driven Attention-refined Recurrent Neural Networks (ALS-Att-RNN) model to identify the public perception key risk factors and key risk chains of different risk levels, as well as to evaluate the public opinion situation. To ensure reliable model performance, data preprocessing was carried out using Z-score normalization, which standardized the input data and enhanced the accuracy of subsequent analysis. An association rule mining algorithm is designed to handle non-Boolean and continuous data, enabling the extraction of significant correlations among misinformation signals, key viral risk indicators, and public perception trends. The framework identifies critical misinformation chains, trustworthy information sources, and key perception risk factors, providing a quantitative evaluation of information authenticity across social and intelligent network platforms. Empirical results show that the proposed method improves assessment accuracy (0.98) compared to conventional models. This framework provides a computationally efficient and interpretable tool for health authorities to identify misinformation chains, key perception risk factors, and public response patterns, supporting timely interventions and guiding informed decision-making to mitigate viral disease risks.
