Research on Online Reading User Clustering Method and Application based on FCM Algorithm

Reading is one of the important ways for people to absorb knowledge and improve their ability. With more and more extensive network information and more and more popular digital reading, people's reading media is no longer limited to paper text. According to the analysis of various surveys, it is found that online reading has a certain impact on the traditional paper book reading. In this paper, online reading users as the research object, and through offline interviews and questionnaires in the form of 362 valid data. The data is processed, the quantitative data is imported into Matlab, and FCM algorithm is used to cluster similar users according to the characteristics of users. The experimental results show that the clustering effect is the best when SSE index determines that the clustering results are four categories, and the characteristics of the four categories of readers are significantly different. This paper analyzes the characteristics of all kinds of readers, makes corresponding recommendations according to the characteristics of different readers and users, and puts forward corresponding promotion countermeasures, so as to improve users' online reading satisfaction and the use frequency of online reading platform.