Design of an Intelligent Teaching Platform under Multidimensional Data Fusion in Music Performance Teaching

This paper first describes the main content and overall goal of the construction of the intelligent teaching platform under multi-dimensional data integration. Then, combined with the main content and overall goal, it implements the construction of the intelligent platform from three aspects: preprocessing of music information, characterization of music, and assessment of sight-singing ability, respectively. Finally, the correct matching rate of the selected chord sequence matching algorithm of the platform is verified through testing, the credibility of the platform’s sight-singing ability score is analyzed, and the students’ satisfaction with the platform is studied through a questionnaire survey. Compared with the other two algorithm models, the Chord Sequence Matching Algorithm improves the correct rate of recognizing chord types by a minimum of 2.35% and a maximum of 11.55%, which is a high correct rate. The experimental class consisting of 15 students verified that the scoring results of steps 1-4 of the platform scoring reached the expected goal of the test case with high credibility in the platform’s sight-singing ability scoring credibility test. The platform was used by the students participating in the experiment with a 100% satisfaction rate. The use of intelligent teaching platforms for music performance teaching can effectively improve the teaching effectiveness.