# Text Classification Model Based on Graph Attention Networks and Adversarial Training
## 1 Introduction  
With the rapid development of technology and the widespread adoption of the internet, people are increasingly active on social networking platforms. This shift has resulted in an abundance of news data [1], which often exhibit strong timeliness and contain potential economic benefits. For instance, e-commerce companies engage in opinion mining and sentiment analysis of user reviews to discern genuine customer needs and promptly refine their products. Such time-sensitive textual data can provide significant value to businesses, thereby making news text classification a popular area of research. Text classification approaches can be primarily divided into two major categories: traditional machine learning methods and deep learning-based methods. Compared to traditional machine learning algorithms, deep learning methods exhibit significantly enhanced expressive capabilities. Currently, the integration of deep learning with text classification has achieved substantial progress. Traditional machine learning classification models primarily rely on feature engineering, including techniques such as the bag-of-words model [2] and n-grams. Representative algorithms in traditional machine learning include Naive Bayesian (NB) [3], Support Vector Machine (SVM) [4], and Maximum Entropy models [5]. Deep learning-based text classification models are capable of automatically extracting features and performing end-to-end learning, as well as capturing the underlying semantic information of texts. [PERSON] et al. [6] introduced the Word2 Vec model, which represents words as low-dimensional, dense vectors and uses the distances between these vectors to measure word similarity, thus capturing semantic information inherent to the words themselves. [PERSON] et al. [7] proposed the TextCNN model for text classification, utilizing Word2 Vec pre-trained word embeddings. This model has achieved favorable results in text classification tasks, although it falls short in representing local textual information and contextual relationships. [PERSON] et al. [8] developed a Recurrent Neural Network (RNN) for text classification that is capable of capturing historical information in text sequences and effectively handling contextual data, thus aiding in the extraction of deeper textual features. However, RNNs tend to lose information when processing long texts. [PERSON] et al. [9] proposed two types of attention-enhanced Bi-directional Long-Short Term Memory networks (Bi-LSTM) to improve classification performance, achieving notable results across multiple text classification tasks. [PERSON] et al. [10] introduced the attention mechanism into RNN models to address alignment challenges in sequence-to-sequence models and the poor performance with long texts. This mechanism [11] allows the model to handle varying input sizes and focus on the most relevant parts of the input, proving highly effective in tasks such as machine reading and learning sentence representations. The attention mechanism enhances the representation of weights in the feature learning process, clearly indicating the significance of each word in classification predictions, and has thus garnered extensive attention in the academic community. For example, [PERSON] et al. [12] integrated the attention mechanism into a hierarchical multi-channel structure model to extract more significant subjective information from texts. [PERSON] et al. [13] proposed an end-to-end adversarial memory network that automatically captures keywords and further introduced the Hierarchical Attention Transfer Network (HATN), which enables the differentiation between core and non-core feature localization. In recent years, Graph Neural Networks (GNNs) have also demonstrated commendable performance in text classification tasks. [PERSON] et al. [14] introduced the Graph Convolutional Network (GCN), which employs a semi-supervised approach to learn structural features within graphs and has achieved favorable results in node classification tasks. [PERSON] et al.