A study on the improvement of student education management in universities based on depth-constrained Boltzmann machine

Abstract

Abstract In this paper, a student education management platform is constructed using ASP.NET development technology. Secondly, a fast CD method is used to update the main parameters of RBM, and multiple restricted Boltzmann machines are used to jointly form a depth-constrained Boltzmann machine for the personalized recommendation of educational management methods. Finally, a performance test analysis was conducted to verify the platform’s effectiveness presented in this paper. The results show that when the number of concurrent users is 600, the maximum response times of student management, faculty management and education management functions are 2.79s, 2.95s and 2.81s, respectively, and the CPU occupancy rate is less than 20%. The education management platform with good performance can help improve and innovate student education management.