LAUSR

Music is closely related to people's lives, and it has a certain impact on people's lives. In school teaching activities, mastering the skills of playing musical instruments can effectively improve students' music appreciation ability and level and enhance students' comprehensive quality through subtle influence. Based on the analysis of students' behavior data, this paper analyzes the role of mastering musical instrument playing skills in improving students' comprehensive quality and puts forward research ideas and schemes. It focuses on students' group behavior in the digital campus environment, integrates multisource data in the digital campus, quantificationally calculates students' multidimensional behaviors, studies the behavior rules of students with different academic performance levels, and uses machine learning algorithm to build a multifeature integrated model of students' comprehensive quality, providing personalized feedback for the improvement of students' comprehensive quality. The results show that the effect of mastering musical instrument playing skills combined with data mining analysis of students' behavior is generally 30% higher than that of the previous research. Compared with a single model, the fused model can fully consider each algorithm to observe data from different data spaces and structures and give full play to the advantages of different algorithms. The training of a single model will fall into the local minimum, which may lead to the relatively poor generalization performance of its model. However, the weighted fusion of multiple basic learners can effectively reduce the probability of falling into the local minimum.