# Knowledge-Graph- and GCN-Based Domain Chinese Long Text Classification Method
## 2 Related Work
[20] proposed BertGCN, which uses BERT to encode the input text. BERT is massively pre-trained to give words a semantic information-rich vector initially and learn dynamic word vectors to solve the problem of multiple meanings of words. The edges are connected between words and documents. The weights of edges are determined by TF-IDF and PPMI. However, PPMI only measures the co-occurrence relationship between words and lacks sufficient semantic information. To address the above problems, [PERSON] et al. [21] proposed A-GAN to use syntactic dependency information relations as edges between tokens while adding syntactic dependency types to GCN, and considering the weight relations between individual tokens in the attention matrix to further enrich the edge information of graph neural networks. The above Chinese long text classification methods extract semantic information mainly based on text features; ignoring domain knowledge leads to weak deep text relevance and limits the text classification performance. A knowledge graph is a kind of knowledge system that represents knowledge in a structured way and is mainly used to describe various entities and concepts that exist in the real world and the relationships among them. Many studies combine knowledge graphs with pre-trained models or deep neural networks to achieve text classification [22; 23; 24; 25; 26]. [PERSON] et al. [27] solved the problem of small data samples by injecting sentiment domain knowledge into a linguistic representation model to exploit the additional information in sentiment knowledge graphs and obtain the embedding vectors of entities in sentiment knowledge graphs and words in a text in a consistent vector space. [PERSON] et al. [28] proposed a two-level filtering model for long text classification in science combined with a knowledge graph to reduce the interference of topic-irrelevant information and improve the classification performance of the model. [PERSON] et al. [29] proposed a knowledge graph augmentation network (KGAN) that learns contextual and syntactic representations in parallel to fully extract semantic features, and then integrates the knowledge graph into the embedding space, based on which aspect-specific knowledge representations are further obtained through an attention mechanism that captures sentiment feature representations from several different perspectives. [PERSON] et al. [30] targeted the specificity of Chinese text, obtained the vector representation of the sentiment knowledge triad through the TransE model, deeply fused it with the feature vectors obtained from the BiGRU and attention mechanism models, and proposed a method for sentiment analysis of Chinese text with multi-granularity semantic features driven by knowledge and data synergy. Numerous experiments have proven that introducing knowledge graphs into natural language processing enables models to extract additional prior knowledge from knowledge graphs, and also solves the problem of small data sample size by knowledge introduction, which is very effective for improving model performance. In general, there are three problems in the existing research related to long Chinese text classification techniques, as follows: (1) Long text length--due to long text lengths and the extensive and scattered key information, long sequence processing tends to ignore rich semantic information in the text hierarchy. (2) Lack of expert domain knowledge--the pre-trained model requires certain a priori knowledge, and relying on text similarity alone can cause the problem of inaccurate classification. (3) Classification accuracy is not high--because the text of the dataset exists in various domains with different lengths and features, the robustness of the existing model still needs to be improved, resulting in biased classification results. Improvement methods should be proposed mainly around these problems.