Conclusion
This paper proposes the PEGCN model, which fully utilizes the advantages of large-scale pre-trained models and graph convolutional networks for text classification. The model first uses input representations with positional information to enable the network to learn relative position information between text; then processes the adjacency matrix to extract edge features fully; meanwhile, uses the BERT model for auxiliary training; finally, combines the predictions of the two models using linear interpolation for classification. Through a series of experimental designs and comparative analyses, it is found that the proposed method in this study outperforms other methods on five commonly used public datasets, achieving the highest classification accuracy and demonstrating the effectiveness of the model. Also, through a series of effectiveness experiments, it is proved that adding positional information and extracting edge features in graph convolutional networks are effective for improving classification accuracy. In future research, this study will further explore the improvement space of this network.