为了满足用户日益增长的感性需求，提升产品的满意度，本研究基于在线评论数据进行用户感性需求的挖掘研究。首先，以产品在线评论为数据源，通过网络爬虫获取评论数据中的感性需求；然后，通过关键词提取算法和聚类算法采集在线评论中具有代表性的用户感性需求；最后，基于感性工学技术对用户感性需求进一步展开分析，进而推测出符合用户需求的产品特征。将本文所提的用户感性需求挖掘研究方法应用在智能手表的感性需求挖掘中，验证了该方法的可行性。文章所提的研究方法能够快速从在线评论数据中挖掘到用户真实的感性需求，为感性工学中感性数据库的构建提供全新的信息输入渠道。同时，也为其他传统工业产品的设计提供思路的借鉴和参考。 In order to meet the increasing kansei needs of users and improve product satisfaction, this re-search is based on online review data to conduct research on user kansei needs. First, use online product reviews as the data source to obtain kansei needs in the review data through web crawlers; then, collect representative kansei needs of users in online reviews through keyword extraction algorithms and clustering algorithms, which are represented by adjectives; finally, based on the kansei engineering technology, further analyze the user’s kansei needs, and then infer the product characteristics that meet the user’s needs. The user kansei demand mining research method proposed in this paper is applied to the kansei demand mining of smart watches, and the feasibility of this method is verified. The research method proposed in the article can quickly mine the real kansei needs of users from online comment data, and provide a new information input channel for the construction of perceptual database in kansei engineering. At the same time, it also provides reference for the design of other traditional industrial products.