%A Zhou Zeyu,Wang Hao,Zhao Zibo,Li Yueyan,Zhang Xiaoqin %T Construction and Application of GCN Model for Text Classification with Associated Information %0 Journal Article %D 2021 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2021.0266 %P 31-41 %V 5 %N 9 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_5158.shtml} %8 2021-09-25 %X

[Objective] This paper tries to learn the text contexts and the polysemy of words, aiming to improve the performance of automatic text classification. [Objective] We proposed a GCN model for long text classification with associated information. First, we used BERT to obtain the initial features of word vectors of the long texts. Then, we input these initial features into the BiLSTM model to capture their semantic relationship. Third, we represented the word features as nodes of the graph convolutional network SGCN. Fourth, we used the vector similarity between words as the edge to connect the nodes, and construct a graph structure. Finally, we input the long text representation from SGCN into the fully connected layers to finish the classification tasks. [Results] We examined our model with Chinese scientific literature having multiple subjects. The accuracy of our model is 0.834 09, which is better than the benchmark model. [Limitations] We only treated the texts as single topic ones for multi-classification tasks. [Conclusions] The proposed model based on BERT, BiLSTM and SGCN algorithms could effectively classify long texts.