An Empirical Analysis of Piano Performance Skill Evaluation Based on Big Data

Abstract

Teachers often guide the rhythm and coherence of piano performance in the teaching process. It is of great significance to use computer technology to automatically evaluate piano performance skills. In this paper, computer technology is used to automatically evaluate the accuracy of piano music classification based on the high-dimensional data collaborative filtering recommendation algorithm, and the K-means model algorithm is used for comparative testing. By comparing the classification results of the high-dimensional data collaborative filtering recommendation algorithm with the piano music classification results of the K-means algorithm, the piano learning burden can be reduced and the piano learning effect can be improved. The research results of this paper show that the accuracy rate of the automatic piano performance evaluation system based on the high-dimensional data collaborative filtering recommendation algorithm reaches 95%, which has a good evaluation effect.