This study seeks to address persistent challenges in traditional music education, particularly the subjectivity inherent in assessment criteria and the frequent delays in feedback, by harnessing the capabilities of artificial intelligence (AI) in conjunction with mobile internet technologies. The primary objective is to design and implement an intelligent, standardized system capable of evaluating musical performances with greater precision and efficiency. To achieve this, the study leverages the WeChat public platform as a foundational interface, upon which a comprehensive, multidimensional intelligent evaluation framework is constructed. This system incorporates advanced deep learning algorithms to facilitate automated and accurate analysis of various instrumental performance techniques. A robust system architecture has been developed, comprising several integrated modules, including user management, a curated digital music library, and intelligent evaluation components. At the algorithmic core of the system lies a neural network–based method tailored for extracting audio features and assessing performance quality, thereby enabling a seamless closed-loop workflow that spans from real-time data acquisition to the generation of intuitive, visualized feedback for learners. Experimental validation confirms that this intelligent evaluation system significantly surpasses conventional methods in terms of both note-level detection accuracy and consistency in full-piece performance scoring. Moreover, the system’s auto-play functionality has demonstrated an accuracy rate exceeding 60%, effectively mitigating the prevalent issues of evaluator inconsistency and delayed response in traditional assessment approaches. The key innovation of this study lies in its dual contribution: From a technological standpoint, it integrates professional-grade music performance analysis into a mobile-accessible format. From an educational perspective, it offers objective, prompt, and actionable feedback to learners. Overall, the system exemplifies a forward-thinking application of AI in the field of music education, enhancing instructional quality and advancing fairness, personalization, and adaptability within modern pedagogical contexts. Its implementation carries substantial practical significance for improving both the accessibility and equity of music education in diverse learning environments.
Minhui Li (Sun,) studied this question.