[Purpose/significance] Enrich and expand the theoretical research system of building dynamic portrait of social academic App users based on small data, so as to provide ideas and reference for the social academic App platform to effectively predict the evolution trend of user behavior and improve the precise service level.[Method/process] Firstly, based on the deep analysis of concept and characteristics of the small data, combined with the feature of social academic App, this paper from two aspects of user behavior and the surface of deep factors designed dynamic portrait label system. Then collected the small data with strong correlation and high value with the user as the data support of the portrait, and clarified the acquisition and processing method. Finally, it put forward the research method to realize the dynamic portrait and form the overall frame model.[Result/conclusion] The construction of dynamic portrait of social academic App users based on small data can effectively refine the granularity of portrait, and improve the lag of previous portrait, which has important reference value for the promotion of accurate service level of social academic App platform under data-driven situation.
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