## 2 Related Work
### Text Classification Method  
Earlier researchers first encoded the text by pre-trained word vectors and frequency-based TF-IDF. Then the encoded text vectors were processed by different combinations of neural networks and fed into a classifier consisting of fully connected layers to obtain the final classification results. For example, [PERSON] et al. [18] used CNNs to solve text classification tasks on social media, [PERSON] et al. [19] applied LSTM and BILSTM to classify Urdu text, and [PERSON] et al. [20] used sentiment analysis of text using dense feed-forward neural networks. [PERSON] et al. noticed that the features extracted using the neural network were only interested in the presence or absence of objects but not sensitive to the orientation aspect of the objects in the picture, so they proposed the capsule network [21]. [PERSON] et al. [22] used the capsule network for natural language processing tasks and obtained good results, proving that it was also adequate for text classification tasks. [PERSON] et al. [23] found that traditional deep learning methods are good at processing Euclidean space data but could be better for the more common non-Euclidean space data in life, often referred to as graphs. Hence, researchers proposed graph convolutional neural networks (GCN) to process graph data. Since the GCN was proposed, researchers have quickly applied it to text classification tasks with promising results [24; 25; 26; 27]. Researchers have then applied different neural networks to downstream tasks and found that individual neural network models are deficient in certain aspects. For example, CNN-based models are not sensitive to temporal information, RNN-based models cannot compute in parallel, and Transformer-based models are deficient in local feature extraction [28]. So researchers started combining different neural networks to compensate for their respective deficiencies. [PERSON] et al. [29] combined multi-headed attention with BiLSTM and BiGRU for medical text classification, and [PERSON] et al. [30] combined CNN and attention mechanisms to achieve good results on text classification tasks. Both the neural network models alone and the composite models combining different models use pre-trained word vectors, such as Glove [31] and Word2 Vec [32], to encode text data,which is a static encoding. The word vectors only contain the information of a single word but cannot be combined with contextual information. With the ELMO [33] model and large-scale pre-training models such as BERT [4] and CLID [5] being proposed one after another, the concept of dynamic coding has gradually come into the view of researchers. Unlike static coding, which can only encode the word itself, dynamic coding will combine the contextual information in the sentence to encode the word. so the word vector contains not only the meaning of the word itself but also the contextual information. Moreover, this coding method solves the ambiguity problem to a certain extent. If these pre-trained models are fine-tuned in downstream tasks, they can encode words into word vectors that the model more easily understands. Since this encoding approach was proposed, many researchers have migrated pre-trained models to text classification tasks by transfer learning methods. They have performed well in several tasks, even obtaining SOTA results [34; 35; 36; 37]. Adding other neural network models to dynamic coding also gives good results [38; 39; 40]. However, this coding approach still has some limitations, as these pre-trained models need to be fine-tuned using the labeled data from the downstream task to achieve good results when applied to the downstream task. The CLIP model proposed by [PERSON] et al. [41] makes researchers pay attention to the concept of zero-shot. The so-called zero-shot is to directly apply the model to downstream tasks without using any downstream task data for training. All the above approaches, except the CLIP model, can only process data in text form.