Research on User Preferences of Chinese AI Piano Education Software Based on AHP-TOPSIS Model

In recent years, the application of artificial intelligence (AI) technology in music education has been steadily advancing, with AI piano education software gradually becoming an essential tool for learners.However, significant disparities exist among major platforms regarding interface design, teaching content, feedback mechanisms, personalized customization, and interactive experience.To address these issues, this study adopts the AHP-TOPSIS model to construct a user preference evaluation system and systematically analyze the strengths and weaknesses of mainstream AI piano education software in China.First, a literature review and user interviews extracted five key dimensions and 17 specific indicators based on user concerns.Then, using purposive sampling, 12 experts were selected to complete a questionnaire based on a 1-7 Likert scale.A judgment matrix was constructed based on expert ratings, and consistency checks were conducted.The AHP method was employed to calculate the weight of each indicator, revealing that interface usability (47.5%) and teaching content effectiveness (29.1%) were the most critical factors influencing user preferences.Based on this, 450 user feedback samples were collected, and the TOPSIS method was applied to calculate the relative closeness of each platform to the ideal and negative-ideal solutions, ultimately determining the platform rankings.The results show that Platform D1 and D2 performed exceptionally well in course design, feedback accuracy, and personalized recommendations, with closeness coefficients of 0.841 and 0.792, respectively.In contrast, Platform D3 and D4 demonstrated noticeable deficiencies in response speed and interactive experience.This study provides data support for optimizing AI piano education software.It offers concrete suggestions for improvement, such as enhancing AI feedback accuracy, optimizing personalized learning paths, and improving multi-device adaptability.The research findings provide a theoretical foundation and empirical reference for developing intelligent education systems and promoting progress in this field.