Abstract: Currently,social media platforms have become crucial sources of information for identifying campus public opinion events.However,the analysis of such events still faces unique challenges,including sparse domain terminology,platform-specific structures,and diverse event types.To address these issues,this paper proposes a multimodal fusion framework based on graph neural network(GNN).By integrating DOM topological structures,knowledge-enhanced text,and cross-modal dynamic interactions,the framework provides campus administrators with a highly robust and accurate public opinion analysis tool.The approach enhances the robustness of public opinion analysis by mining the topological semantics of HTML DOM and cross-modal dynamic interaction information to obtain deeply fused feature representations.The model parses the DOM tree into a multi-level graph structure and employs GNN to model topological relationships between nodes.It incorporates a gated cross-modal attention mo-dule to dynamically adjust the fusion intensity among DOM,text,and visual modalities.Additionally,a Wikipedia knowledge enhancement strategy is introduced to expand short-text semantics through entity associations.Experimental results demonstrate that the proposed model achieves improvements over baseline models across multiple benchmark datasets.This framework effectively addresses issues such as semantic fragmentation across modalities and insufficient information in short texts,delivering a high-precision multimodal fusion solution for campus public opinion analysis.

Key words: Multimodal analysis, Knowledge enhancement, Graph neural network, Cross-modal fusion, HTML DOM