College Student Social Dynamic Analysis and Educational Mechanism Using Big Data Technology.

Abstract

With the advancement of the “big data” technology, college students inadvertently purchase personal advice while taking advantage of the exciting Internet to access information quickly and easily. In order to objectively achieve the real office of college students' material enlightenment penetration in the mobile-friendly network, we choose the popular mobile social network, and we apply the natural clustering algorithm rules to segment the college students. Further, we identify college students, based on which we construct information leakage and apply the risk assessment design. The comprehensive entrepreneurial evaluation of the microblog platform combined with the user's mobile complaints is utilized to conduct a psychological analysis on the key components and key communication channels of college students' complaint leakage. We obtain ticket data using the social prospect method and refer to four dogmatic characteristic elements of query motivation. And we also collect dimensions through surrogate analysis. Based on the reference feature factor, four different user groups are rapidly moved. Dining, social, teaching and large users, and the model features of various usage profiles are described in combination with eight categories of user feature importance. The |objective is to improve college students' awareness of social network and mobile partner network mobile information to a certain extent. We attempt to protect college students from fraud and installation plans, to standardize the management of online social platform of advertisements, and to progressively promote the disposal of network movable property due to security changes.