A Model for Integrating and Reconstructing Music Curriculum for Higher Education Based on Deep Learning

Abstract

In this paper, firstly, the problem is modeled by a formal description of the problem of resource fragmentation in music courses for teachers, and the problem is simplified based on regional division. Secondly, an FFD-basic migration matching algorithm is proposed, and the migration matching of teaching resource nodes is realized by the least resource maximum flow model tuning. Then the measurement model of the music teaching ability of teachers was constructed, and the teaching ability measurement scale was designed. Finally, the effectiveness of the curriculum integration and reconstruction model was proved through empirical effect and analysis. The results show that the training speed is effectively increased by about 0.25 with the deep learning based model of curriculum integration and reconstruction for teachers in music. This paper provides a new solution for the integration of teaching resources in the field of music education for teachers, which has important theoretical and practical significance.

Keywords

• higher education music curriculum
• resource fragmentation
• ffd-basic
• migration matching algorithm
• integrated reconstruction model
• 00a65