# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 1 Introduction  
Text classification is one of the most fundamental tasks in natural language processing and is the basis for recommendation tasks, question-and-answer systems, sentiment analysis, and many other tasks. The text classification referred to in this paper is to multiclassify texts according to topics into multiple categories, such as financial, sports, military, social, etc. As the amount of text data increases geometrically, the study of text classification tasks, which are the basis of many applications, has become more and more important. As Chinese and English belong to different language families, the minimum units of text composition are different (English takes letters as the minimum units, and Chinese takes Chinese characters as the minimum units); in addition, Chinese needs to be processed by word separation, while each word in English is separated by spaces. This directly affects the transferability of English methods. In recent years, domain long text classification has received a lot of attention from researchers and has made great progress, but most of the existing studies focus on the data dimension, which means extracting deep semantic representations of Chinese text by deep learning models to obtain contextual information, making it difficult to solve the problem of multiple meanings of words in different contexts and leading to problems such as ambiguous semantic understanding and inadequate feature representation. In terms of text vectorization representation, BERT can obtain dynamic word vectors to better represent semantic information, but since the research content of this paper is long text classification, the text length is long and much larger than the maximum input sequence length of BERT. If truncated text is used to make it meet the input sequencelength requirement of BERT, a large amount of information in the text will be lost and the accuracy of classification will be reduced. On the one hand, it will consume a lot of hardware resources; on the other hand, it will consume a lot of computational time because the computational complexity grows with the square of the input text length if the length of the positional embedding is increased and retrained from scratch. In terms of classification model construction, the long text classification task causes many text classification models to fail to perform optimally due to the long text. For text classification algorithms using only convolutional neural networks, the convolutional kernel field of view is controlled by the size of the convolutional kernel, so only local features can be extracted, and longer field of view features cannot be focused on; for text classification algorithms using only recurrent neural networks, the memorability of the internal memory network gradually decreases as the input text length becomes longer for long texts. Although the combination of convolutional and recurrent neural networks can solve the respective drawbacks of the above two models, however, all the above text classification models ignore the unique structured features of long texts themselves. In addition, long texts can extract more features compared to short texts, but they also have more redundant words, which are irrelevant words that have no positive impact on the classification structure and tend to affect the classification accuracy. To address the above issues, a domain long text classification model based on a knowledge graph is proposed for the above problems. At the data dimension, dynamic word vectors are trained using BERT to enrich semantic information, and GCN is used to obtain long-distance text semantic dependencies. At the knowledge dimension, prior knowledge is introduced through the knowledge graph to solve the problem of multiple meanings of words in different contexts. The main idea of the model is, firstly, the data text is encoded using BERT to obtain the initialization vector containing rich semantic information, and each word's corresponding vector is used as the node of GCN.