# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 1 Introduction
Firstly, traditional Convolutional Neural Network (CNN)-based text categorization models struggle to capture long-distance dependencies and global semantic information in text due to their confinement to fixed-size input windows. They face challenges in effectively handling variable-length texts and intricate sequential relationships between words. Secondly, while models leveraging attention mechanisms can dynamically focus on crucial parts of the text, this often escalates computational complexity and necessitates substantial data to train effective attention weights, occasionally leading to overfitting issues. Furthermore, despite BERT's proficiency in capturing global semantic information in text, its extensive computational demands and storage requisites restrict its application in resource-constrained environments. To address these challenges, the BertGCN-Fusion (BGF) model proposed in this paper offers an innovative solution. By introducing multiple linear layers, the BGF model effectively integrates the fine-grained local information learned by the BERT model with the global relational information learned by the GCN model. This feature fusion strategy not only addresses the traditional models' shortcomings in processing global semantic features but also enhances the model's generalization ability and classification performance. * The design of the BGF model is based on the following theoretical foundations  
Graph Convolutional Neural Network: The GCN is adept at capturing local structural information among nodes. Through its message-passing mechanism, the features of each node not only contain its information but also integrate information from neighboring nodes. However, the GCN has limitations in the depth of information propagation and suffers from the loss of global semantic information due to local feature fusion when dealing with global semantic features. BERT model: The BERT model is a pre-trained language model that captures fine-grained semantic features in the text through bidirectional encoder representations. BERT learns rich contextual semantic information from large amounts of textual data during pre-training, giving it powerful semantic understanding capabilities. However, BERT mainly focuses on local features and is less effective in capturing the global structural information of the text. Feature fusion mechanism of BGF model: The BGF model fuses the fine-grained local semantic features output by BERT with the global structural features captured by the GCN by introducing a linear layer. This feature fusion mechanism allows the BGF model to compensate for the GCN's shortcomings in capturing global semantic features while retaining the semantic understanding advantages of BERT. Multilayer fusion: The BGF model adopts a multilayer fusion approach, which enables the model to capture the semantic information in the text more comprehensively and accurately by integrating textual semantic features at multiple levels. This multilayer fusion mechanism enhances the richness and accuracy of feature representation. Specific advantages of the BGF model  
R8 dataset: On the R8 dataset, the BGF model achieves an accuracy of 98.45%, which represents a 0.28% improvement compared to the BertGCN model. This example highlights the BGF model's outstanding performance in news text categorization, affirming the effectiveness of its feature fusion mechanism and multilayer fusion approach. R52 dataset: The BGF model achieves an accuracy of 93.77% on the R52 dataset, showing a 0.90% improvement over the BertGCN model. This outcome demonstrates the BGF model's effectiveness in larger-scale text classification tasks by effectively integrating local semantic information and global structural features. MR dataset: Although the accuracy and loss function of the BGF model on the MR dataset show considerable fluctuations, further model tuning and optimization are expected to enhance its performance in sentiment classification tasks.