Abstract：［Purpose/significance］ Academic social networking sites have become an important way for researchers to maintain interpersonal relationships and conduct scientific research cooperation and academic exchangesCarrying out user behavior clustering research is significant for academic social network platforms to accurately identify user components,understand user behavior and improve service efficiency［Method/process］ Taking ResearchGate(RG) as the research object,this paper used kmeans algorithm to cluster and analyze the behavior of users on academic social networking sites based on the construction of a descriptive model of user behaviorBesides,this study explored the distribution and the behavioral characteristics of users from the perspective of disciplinary differences［Result/conclusion］ The results show that RG users can be divided into 10 categories,with different user groups having different preferences in the use of platform’s functions;meanwhile,there exists disciplinary differences in the usage behavior of users of academic social networking sitesTo be specific,natural sciences’ users are more evenly distributed,less extremely biased towards one user's group and more active in their usage behavior,while humanities and social sciences’ users are mainly composed of lurkers who are more reticent
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