# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 1 Introduction  
The task of text categorization is a crucial research direction in the field of natural language processing (NLP), which is widely used and has significant application value in email categorization [1], data mining [2], intelligence analysis [3], and sentiment classification [4]. Traditional machine-learning-based text classification algorithms, including Plain Bayes [5], Support Vector Machine (SVM) [6], and Decision Tree [7] algorithms, typically involve converting text into feature vectors. However, with changes in training samples, classification results may fluctuate or even deteriorate. With the rapid advancement of deep learning, the BERT (Bidirectional Encoder Representations from Transformers) model [8], which is a deep learning model, was created by Google AI Language researchers. This model has attained remarkable success in natural language processing [9], prompting an increasing number of researchers to apply it to text classification. Deep learning models can improve a model's generalization ability compared to traditional models by learning to generate high-dimensional embedded representations of semantic and syntactic information. Text classification methods based on deep learning can be broadly categorized into three types: text classification models based on a Convolutional Neural Network, text classification models based on the attention mechanism, and text classification models based on BERT. The text classification model based on Convolutional Neural Networks effectively captures local features in the text and performs well with long text inputs. Theconvolution operation can be computed in parallel, reducing the number of parameters that need to be learned and thus decreasing model complexity and the risk of overfitting. However, it may lose some positional information, fail to capture the sequential relationship between words, and require fixed-size input. Text classification models based on the attention mechanism enable dynamic focus on important parts of the text, facilitating better modeling of contextual relationships between words. They can handle text inputs of varying lengths. However, they increase the computational complexity of the model and sometimes require more data to effectively train the model and obtain meaningful attention weights. BERT-based text categorization models excel in capturing global word relationships and possess strong semantic understanding. However, they typically incur higher computational and storage costs, demanding longer training times and larger computational resources. In recent years, Graph Neural Networks (GNN) [10] have been increasingly employed in text categorization due to their capability to capture rich and effective relational structural information while considering the global context of documents [11]. However, GNN are limited in their ability to perform convolutions on data structures in non-Euclidean space. To address this, [PERSON] introduced the Graph Convolutional Networks (GCNs) [12]. As Convolutional Neural Networks continue to evolve, Graph Convolutional Neural Networks are being applied to tasks such as text classification. The TextGCN [13] model integrates a Convolutional Neural Network with text classification tasks. It constructs a heterogeneous graph using word frequency across the entire corpus and utilizes BERT to extract semantic information from text nodes. This approach addresses the challenge of combining pre-trained models with a Graph Neural Network efficiently. BertGCN [14], on the other hand, focuses solely on providing semantic embeddings at the sentence level through label extraction during training, omitting other semantic aspects of the sentences. In current text categorization research, despite the existence of multiple state-of-the-art models such as the BERT, GCN, and their combined variants (e.g., BertGCN and TextGCN), several limitations still require addressing.