Abstract
This paper proposes an enhanced BertGCN-Fusion (BGF) model aimed at addressing the limitations of Graph Convolutional Networks (GCN) in processing global text features for text categorization tasks. While traditional GCN effectively capture local structural features, they face challenges when integrating global semantic features. Issues such as the potential loss of global semantic information due to local feature fusion and limited depth of information propagation are prevalent. To overcome these challenges, the BGF model introduces improvements based on the BertGCN framework: (1) Feature fusion mechanism: Introducing a linear layer to fuse BERT outputs with traditional features facilitates the integration of fine-grained local semantic features from BERT with traditional global features. (2) Multilayer fusion approach: Employing a multilayer fusion technique enhances the integration of textual semantic features, thereby comprehensively and accurately capturing text semantic information. Experimental results demonstrate that the BGF model achieves notable performance improvements across multiple datasets. On the R8 and R52 datasets, the BGF model achieves accuracies of 98.45% and 93.77%, respectively, marking improvements of 0.28% to 0.90% compared to the BertGCN model. These findings highlight the BGF model’s efficacy in overcoming the deficiencies of traditional GCN in processing global semantic features, presenting an efficient approach for handling text data.