[Objective] To explore the development trend of online health communities under public health emergencies, the paper constructs a post popularity evaluation model based on Topsis approach, and uses user portraits to define user roles. [Methods] Crawling the posts related to the epidemic situation in Dingxiangyuan, obtaining 4,972 pieces of valid data, using the Topsis entropy method to rank the popularity of the posts, then using factor analysis to reduce the dimensionality, and finally constructing user portraits based on K-means clustering. [Results] During the epidemic, Dingxiangyuan users posted posts in four major sections: postgraduate entrance examination, news hotspot, mood station, and preventive medicine. We used user portraits to divide users into 7 categories, such as high-influence users, professional users, long-term users, high-volume users, high-potential users, institutional users, and strong interactive users. [Limitations] Because the selected website only allows crawling of the first 14 pages of data, the data set constructed is small, and the horizontal comparison of different OHCs has not been performed. [Conclusions] The research shows that accurate user positioning helps to understand the differences between user groups and accurately grasp user needs during public health emergencies, so as to provide more evidence and suggestions for the community to carry out work under similar incidents.