## 1 Introduction  
In recent years, with the rapid development of information technology and the comprehensive promotion of healthcare information, online medical platforms have become an important part of modern medical services. According to the \"14 th Five-Year Plan for Healthcare Informatization of the Whole Population\", health information is a key factor in promoting the innovation of medical and health services and improving public health [1]. Online medical platforms provide patients with convenient avenues for medical servicesthrough internet technology [2], enabling patients to obtain professional medical advice through online consultation, which greatly improves the accessibility and convenience of medical services. This model not only reduces the time cost for patients to travel to hospitals but also alleviates the problem of uneven distribution of medical resources, which is especially significant in remote areas and regions with scarce medical resources. One of the core functions of online medical platforms is to provide users with a more convenient channel for consultation [3; 4]. Users can describe their symptoms or health problems through the platform, and the platform recommends suitable departments or doctors based on the information provided by users. However, consultation messages are usually short text, characterized by sparse features, unclear semantics and high requirements for professionalism. The limited extent of vocabulary in short text makes it difficult to capture enough semantic information, and the expression is often not specific enough, which may lead to ambiguity in categorization. In addition, short text in the medical field involves a large number of specialized terms, which puts higher requirements on the model's semantic understanding and classification ability.  
Some existing short text categorization methods (e.g., TF-IDF, etc.) [5; 6; 7] have achieved certain results, but they are still deficient in dealing with rare words and contextual information. For example, TF-IDF cannot capture the rarity of words. Deep learning models (e.g., BERT and RCNN) have demonstrated strong performance in text categorization tasks, but their performance largely depends on the feature richness and semantic clarity of the input data. Therefore, how to enhance the semantic information of short texts by keyword expansion techniques and combine them with deep learning models to realize efficient department recommendation has become an urgent problem for online medical platforms.  
Users of online medical platforms usually need to select a doctor of a certain department on their own, but due to a lack of comprehensive understanding and medical expertise, they often register with the wrong number and find the wrong doctor. The triage staff guidance method of offline hospitals is not applicable to online consultation platforms, which will increase the platform operation cost and the time cost of patients' online consultation, in addition to reducing the convenience and timeliness of the platform. Therefore, the automatic categorization of consultation information through intelligent models and the recommendation of appropriate departments for patients have become an urgent problem for online medical platforms. Accurate department recommendation can not only improve the efficiency of users' medical treatment but also optimize the distribution of medical resources and improve the overall service quality of the platform.  
To this end, this study proposes a short text categorization method combining keyword expansion and deep learning models, aiming to solve the feature sparsity and semantic ambiguity problems of short text categorization in online medical platforms, so as to achieve accurate department recommendation. Enhancing the semantic information of short text by the keyword expansion technique and realizing efficient department recommendation by combining deep learning models can significantly improve the service efficiency and user experience of online medical platforms.