Numerical analysis-oriented Kruskal algorithm for the analysis and integration of effective components of university music pedagogy

Abstract

Abstract This paper constructs a collection of effective teaching methods based on the improved K-Means clustering algorithm for clustering and dividing effective components of music teaching in colleges and universities. By analyzing the personalized content recommendation system, we can construct a recommendation system based on teaching content using information retrieval and filtering techniques. The collaborative filtering recommendation algorithm is used to ensure the accurate placement of teaching content. The Kruskal algorithm is used to find the minimum spanning tree of teaching effective components, and the K-means clustering principle is applied to the division of music teaching effective components, and the cluster of effective teaching components is divided by the clustering algorithm. According to the findings, mind-body integration and the teaching goal of valuing creativity were classified as effective teaching components in music. Personal aesthetics had a 0.6 influence on musical creativity, and a free environment had a 0.3 influence.