## 2 Related Work
### Model Based on Traditional Methods  
At present, in text classification tasks, deep learning technologies have predominantly yielded good results. Traditional deep learning technologies are mainly divided into methods based on CNNs, RNNs, attention mechanisms, and other text classification models. TextCNN [1] is the earliest text classification method with CNNs. The concept of multi-channel convolution proposed by [PERSON] uses pre-trained word vectors word2 vec and multi-channel embedding technology to achieve certain results in public datasets. [PERSON] et al. [2] proposed DPCNN for text classification. They obtained longer semantic information through equal-length convolution and pooling and deepened the number of convolution layers to achieve significant results. To validate the effectiveness of convolutional layers for text semantic understanding, [PERSON] et al. [3] input character-level text vectors and word-level statement vectors into shallow CNNs and DenseNet. They found that the effectiveness is improved when paired with deep convolutional networks. Meanwhile, word-level statement vectors are more suitable for shallow convolutional networks. However, the biggest problem faced by CNNs is that they cannot fully extract the dependencies between longer texts. The research on RNNs has addressed the problem of long-statement dependence to a certain degree, and the development of LSTM [17] and GRU [18] has gradually increased the number of RNNs used for text classification. [PERSON] et al. [5] designed a multi-task LSTM model for text classification with small amounts of data. [PERSON] et al. [6] studied tree networks and designed the Tree-LSTM architecture. They verified through experiments on sentiment classification datasets that the tree structure is superior to the sequential LSTM structure to a certain extent. In [19], [PERSON] et al. simultaneously extracted Chinese character-level vectors and word-level vector representations, built the C-BLSTMmodel, and achieved good results in short-text classification. [PERSON] and [PERSON] [20] comprehensively used a CNN and RNN and developed a CNN-BiLSTM architecture based on skip-gram technology, which achieved certain results in text classification tasks. [PERSON] et al. [21] also combined the advantages of a CNN and LSTM to build a network architecture capable of extracting long-term dependencies and preserving local information. Combined with machine learning classifiers, they achieved certain results in the task of classifying special language emotions. In [22], the bidirectional GRU mechanism is used for convolution layer association to adapt to corpus and feature vocabulary, thus achieving better results in emotion prediction tasks. Text classification methods based on attention mechanisms have become the focus of numerous scholars' research to further explore the dependencies between texts. A hierarchical attention architecture HAN was developed by [23], which divided documents into the sentence level and word level to calculate attention and construct different features, completing the document classification task. [PERSON] et al. [24] constructed a hybrid attention network HANs, simultaneously utilizing character-level and word-level attention to obtain feature vectors, and made achievements in Chinese short-text classification. In [25], [PERSON] et al. used Bi-LSTM and a convolutional neural network to construct an additional attention mechanism, and this mixed attention mechanism performed well in mining text semantic information and achieved good generalization effects on datasets. [PERSON] et al. [26] built a semantic representation network with multi-attention mechanisms, which showed good effects on emotion classification tasks by mining semantic information at different levels. Similarly, the attention mechanism enables [27] us to achieve significant performance improvements in feature perception, thus achieving better discriminant ability. Apart from these methods, numerous other algorithms have attracted much attention from researchers.