# Text Classification Model Based on Graph Attention Networks and Adversarial Training
## 1 Introduction
[15] developed the textGCN model, adapting the graph convolutional network for text classification by constructing texts as heterogeneous graphs, where nodes consist of words and documents, and edges are weighted by term frequency-inverse document frequency (TF-IDF) between documents and words, as well as pointwise mutual information between words, transforming the text classification task into a node classification challenge. [PERSON] et al. [16] proposed the graph attention network (GAT), which utilizes an attention mechanism. This network employs masked self-attention layers to assign different weights to adjacent nodes, enhancing the model's ability to focus on more relevant features in the graph structure, thereby improving performance in tasks such as node classification within text-based applications. [PERSON] et al. [17] propose a Deep Attention Diffusion Graph Neural Network (DADGNN) model that addresses limitations in existing GNN-based text classification methods, such as interaction difficulties between distant words and over-smoothing issues in deep graph layers. [PERSON] et al. [18] introduces heterogeneous graph attention networks (HGATs) for semi-supervised short text classification. The model effectively incorporates different types of nodes and connections, which is particularly useful in scenarios with limited labeled data. [PERSON] et al. [19] introduce a minimum-margin GAT designed to enrich feature information for short text classification, showing significant improvements over existing models like HGAT and STGCN. [PERSON] et al. [20] propose a graphic attention network text classification model that integrates label information, with a focus on integrating label semantic information into GATs for text classification, and enhancing the correlation between text and labels through attention mechanisms. [PERSON] et al. [21] proposed using graph learning to fuse contextual information for text classification, which optimized the fusion of text graphs and contextual information to achieve better document classification and demonstrated the effective integration of word interaction and graph learning. Previous studies have acknowledged the significance of key vocabulary in sentence classification; however, existing models have not fully utilized the boundary information of characters and the semantic information of words. To further enhance the performance of text classification, this paper employs a graph attention network (GAT) that effectively integrates lexical knowledge. The contributions of this work are outlined as follows:  
1. We utilize three distinct graph attention networks (GATs) to extract features from the contextual vocabulary of input text sequences. By concatenating these features with the original input text sequences, we achieve a superior representation of the text. 2. During the model training process, we introduce noise perturbations for adversarial training. Experimental results indicate that the incorporation of noise perturbations enhances the model's generalization ability on original samples and improves robustness. 3. We employ a multi-head attention mechanism, wherein the weight matrices assign higher numerical values to key information. The experiments demonstrate that this approach can further enhance classification accuracy. The rest of this paper is organized as follows: Section 2 presents our network framework. Section 3 provides the experimental results, followed by an analysis of these results. Section 4 concludes the paper.