Analyzing the aesthetic value of college piano in music education using cognitive mapping

Abstract

This paper first analyzes the differentiating factors of piano music education in colleges and universities and constructs a cognitive mapping framework using a fusion graph convolutional network. Secondly, the cognitive mapping of the fusion graph convolutional network was used to extract the cognitive elements of aesthetic value, and then the aesthetic value analysis framework of piano education was constructed. Finally, an empirical analysis was conducted to verify the effectiveness of the aesthetic value analysis framework for piano education. The results showed that the average percentages of A-grade evaluation of the beauty of understanding life, training thinking, exercising ability, and practicing art felt from piano music were 0.494, 0.473, 0.456, and 0.419, respectively, indicating that cognitive mapping can effectively analyze the aesthetic value of college piano in music education.

Keywords

• cognitive mapping
• graph convolutional network
• cognitive elements
• aesthetic value
• piano music
• 97m80