Hierarchical Fuzzy Evaluation Model for Enhancing Student Motivation in Music Learning

In music education, the modeling of students’ learning motivation mechanism in the past lacked a systematic and accurate method, which makes it difficult to deeply analyze the cognitive structure characteristics of learning motivation, resulting in the lack of pertinence in the formulation of educational strategies and the inability to meet students’ personalized learning needs. This study proposes a learning motivation assessment model based on fuzzy theory and evidence fusion, aiming at optimizing music education strategies through cognitive diagnosis. In this study, a five-stage modeling framework was constructed: (1) establishing a hierarchical representation system of curriculum concepts; (2) quantifying concept weights by using fuzzy membership functions; (3) constructing a learning state diagnosis model based on Dempster– Shafer evidence theory, integrating belief parameters and teaching strategies; (4) designing an interpretive diagnosis rule base integrated with fuzzy logic; and (5) constructing a learning state diagnosis model based on fuzzy logic; (5) realize that iterative optimization of the model through expert review. The empirical study adopts the three-dimensional method of system development, simulation test and practical verification, and the results show that the model can effectively analyze the cognitive structure characteristics of learning motivation, and its diagnostic accuracy has obvious advantages over traditional methods. To provide decision support for the development of individualized teaching strategies. Expert validity test shows that the model has significant advantages in terms of concept coverage and strategy applicability. This study provides a new computational paradigm for motivation modeling in the field of music education, and its hybrid intelligence approach is worth generalizing in the field of educational cognitive diagnosis.