## 2 Related Work  
### Basic Network Model-Based Methods  
As an important research direction in the field of NLP, short text classification can extract valuable information from original text data [19]. At first, people used word-matching methods to classify texts, but this simple mechanical method could not bring good classification results. Subsequently, with the help of professionals, a knowledge engineering approach was adopted to define many inference rules for each category [20]. However, this method is susceptible to human factors. In recent years, with the rapid development of big data, cloud computing, and other technologies, people have tried to use machine learning methods to realize the feature embedding of many similar texts [21]. As an important branch of machine learning, deep learning has brought new opportunities for the research of text classification, avoiding the process of the manual design of rules and features. By learning a deep nonlinear network structure, deep semantic representation information of text data can be captured [22], and feature learning of multi-modal data can be achieved. A unified representation of the data can be obtained [23]. Based on the high scalability of deep learning networks, many deep learning networks architectures emerge. Basic deep learning network models include feedforward neural networks [24], recurrent neural networks [25], convolutional neural networks [26], etc. Researchers extract text multi-dimensional features according to the characteristics of different models and achieve remarkable results.  
In 2014, [PERSON] [22] proposed the CNN model for text classification, whose performance is not inferior to that of complex feature learning-based classifier models, which has stimulated the research upsurge of deep learning text classification models. [PERSON] et al. [23] proposed a framework based on convolutional neural networks, which combines the explicit and implicit representations of short texts for classification, enriches text information through word embedding and concept embedding, solves sparsity and ambiguity problems to a certain extent, and improves classification accuracy. [PERSON] et al. [24] proposed a character-level attention convolutional neural network model for short text classification, which uses an attention mechanism to pay attention to text position information and assigns different weights to different characters. [PERSON] et al. [19] proposed a deep learning model combining BERT and BiLSTM to carry out sentiment analysis on online Chinese buzzwords, and the results showed that the deep learning model combined with BERT and BiLSTM performed well in sentiment classification. [PERSON] et al. [26] proposed a model for bidirectional encoder-decoder transformer-convolutional neural network (BERT-CNN) requirement classification. The experiment results demonstrate that the proposed model performs better than the state-of-the-art baseline approach. [PERSON] et al. [27] proposed a feature-enhanced fusion model based on an attention mechanism for Chinese text classification; the results show that the proposed algorithm can significantly improve the recognition ability of Chinese text features. The above methods rely on the context information of the text, and the effect is not ideal for the classification tasks of short texts such as enterprise names.