A Music Teaching Resource Management Model Based on Fuzzy Clustering Algorithm

Abstract

This study develops and implements a music instructional resource platform based on fuzzy clustering algorithm knowledge in order to make the management of music teaching quality more scientific and standardised. This study presents the construction scheme of a music instructional resource management system in a higher education institution through requirements analysis, function design, system development and implementation, and system testing of the software system. In this study, a system is developed that uses computational intelligence to calculate the content and topics contained in resources based on semantic similarity, generate category labels, and cluster educational resources in order to integrate educational resources and implement effective management. It not only meets the needs of users who want to access the platform system via mobile terminals but also increases the platform system’s utilisation rate. Experiments show that this system’s stability can reach 95.37%. The retrieval accuracy is 96.24%, which is 8.94% better than the traditional method. It has the ability to effectively provide high-quality music teaching resources. Using the instructional resource system proposed in this study will improve teaching effectiveness, reduce learners’ learning burden, and increase learning efficiency.