Optimization query algorithm of distance education database based on student behavior analysis

Distance education database query refinement is a pivotal challenge in decentralized database systems. The goal of distributed query execution is to convert an advanced inquiry in a dispersed database into an efficient operational blueprint on a localized database. In this paper, an algorithm framework based on student behavior analysis is proposed on the basis of the current mainstream elective recommendation system. By introducing a large number of students' learning behavior data, students' personality characteristics are mined, students' social relations are calculated, and students' relatively complete behavior portraits are constructed. Data query is carried out according to different data categories, thus realizing database optimization query. Experimental results show that this algorithm can effectively improve the efficiency of database information query.