Design of the College Students’ Music Big Data Management System Based on Computer Assistance

Abstract

With the rapid development of science and technology, the college music big data management system also needs a computer‐aided data model for optimization. In order to improve the efficiency of computer‐aided teaching of the music big data management system in colleges and universities, this paper analyzes and processes the music database of music software based on the computer‐aided Bayesian algorithm and establishes a Bayesian model. Firstly, this paper introduces the principle and function theorem of the Bayesian algorithm. The data is divided into original music and new music, and a college music data management system with search recommendations as the core is established. We iteratively calculate the optimal hidden semantic matrix and we finally select ReLU function as the activation function of this experiment. After the model of the music management system is established, it is applied to a university. The experimental results show that the experiment has reached the expected standard. When the hidden factor dimension is 11, the system model has the best representation of music features. Students’ use of the music search and the recommendation system has greatly improved their music literacy and music coverage.