Research on the Construction and Application of Piano Teaching Evaluation System Based on Performance Ability Development

Piano performance education increasingly requires objective and dynamic evaluation mechanisms capable of tracking students’ long-term skill development. To address the limitations of conventional assessment approaches characterized by excessive subjectivity and insufficient process monitoring, this study proposes a performance-ability-oriented piano teaching evaluation system. An evaluation framework comprising three dimensions—basic skills, performance skills, and creative skills— is established using the analytic hierarchy process and fuzzy neural networks. Entropy-weighting is further employed to determine indicator importance, while visual recognition and decision-tree techniques are integrated for intelligent assessment. A 16-week teaching experiment involving 42 university students demonstrates that the proposed framework achieves an evaluation accuracy of 84.86% and maintains a high correlation (0.892) with expert assessments. Students receiving developmental evaluation exhibited significantly greater improvement in overall performance ability than those in conventional teaching environments. The proposed system provides a data-driven pathway for intelligent music education and offers methodological references for multimodal signal analysis, pattern recognition, and intelligent assessment systems.