A Study on the User Group Classification of Online Q&A Communities Based on Knowledge Sharing Behavior

User classification is crucial for studying knowledge-sharing behavior in online communities. A well-designed classification system can reveal the distinct characteristics and needs of different user groups, enhance the specificity of information retrieval, and ultimately improve the efficiency of knowledge sharing, thereby bringing substantial social benefits to information exchange. This paper utilizes natural language processing techniques to develop an analytical framework based on three dimensions: information entropy, the relevance between questions and answers, and sentiment polarity. A novel user classification method is proposed, categorizing users into four types: routine, knowledge-driven, responsive, and indifferent. The knowledge-sharing value corresponding to each type is calculated and compared. This method not only addresses the limitations of traditional digital feature indices but also, by integrating management theory with quantitative analysis, provides fresh perspectives and strategies for the healthy development of online Q&A communities and the enhancement of knowledge-sharing efficiency.