## 2 Related Work  
### Short Text Categorization Study  
Text categorization is a core task in the field of natural language processing (NLP), which is defined as the classification of textual data into one or more predefined categories according to specific rules or criteria. These categories can be classified based on a variety of dimensions such as topic, sentiment, intent, etc. Text categorization has an extremely wide range of applications, covering a variety of fields such as information retrieval, text mining, intelligent recommender systems, and so on. By categorizing documents, it can help users find the information they need faster and improve retrieval efficiency. With the rapid development of information technology, various industries have generated massive amounts of text data in their daily operations, with short text data being particularly abundant. Given this, the problem of short text classification has gradually attracted the attention of many researchers, making text classification tasks increasingly prominent in the field of natural language processing research. Traditional machine learning methods have been widely applied in the field of text classification, with common algorithms including Naive Bayes, Support Vector Machines (SVMs), decision trees, and k-Nearest Neighbors (k-NN), among others. [PERSON] et al. [8] proposed a novel recommendation algorithm, which cleverly combines the advantages of labeled data and Naive Bayes classification. [PERSON] et al. [9] designed and implemented a Chinese text classification system based on Support Vector Machines. [PERSON] et al. [10] proposed a news text classification method based on KNN. However, these traditional machine learning text classification methods generally rely on manually extracted features, and they have obvious limitations when dealing with large-scale and complex text data. Research on short text classification based on keyword expansion primarily encompasses two approaches: feature expansion based on external knowledge bases and feature expansion based on internal semantics. Research on expansion using external knowledge bases, such as the method proposed by [PERSON] et al. [11], introduces a new approach for expanding short text features using Wikipedia and Word2 vec and, through empirical studies, demonstrates that expanding short text features can effectively improve classification accuracy. [PERSON] et al. [12] proposed a feature expansion method based on a synonym dictionary, which utilizes synonym dictionaries and collocation resources to identify and screen candidate expansion features. These features are then added to the feature vector through semantic relevance assessment, significantly improving short text classification performance. Research on short text classification based on internal semantic expansion has also attracted significant attention from many scholars, such as [PERSON] and [PERSON] [13], who combined LDA with Word2 vec to perform feature expansion on test texts, effectively improving the classification accuracy of the SVN algorithm. [PERSON] et al. [14] enhanced the semantic understanding of medical short texts by combining TF-IDF with Word2 vec, finding that after keyword expansion, the classification accuracy of SVN, MNB, and RF increased by 10%, 9%, and 10%, respectively. It is evident that after keyword feature expansion, machine learning algorithms have demonstrated outstanding performance in text classification tasks. However, with the continuous advancement of deep learning technology, it provides reliable technical support for further improving the accuracy of short text classification. In the field of text categorization, deep learning models are gradually exerting their unique advantages, and their application scope and influence are expanding. Deep learning, on the basis of inheriting the basic concepts of machine learning [15], has been further expanded and deepened.