A method for evaluating the quality of music teaching based on PSO-BP neural network model

To address the subjectivity and uncertainty in traditional music teaching quality evaluation methods, a method based on the PSO-BP neural network model is proposed. This method selects evaluation indicators from multiple dimensions such as teaching ability, learning effectiveness, teaching resources, teaching interaction, and music emotion. By optimizing the initial connection weights and thresholds of the BP neural network through PSO algorithm, an objective, accurate, and efficient music teaching quality evaluation system is constructed. Compared to traditional methods, it not only significantly enhances objectivity and accuracy but also further refines the evaluation criteria by introducing multi-level evaluation dimensions such as technicality, motivation, teaching, interactivity, artistry, and standardization. The case analysis shows that this method performs well in practical applications, and the evaluation results are closer to reality, providing strong support for the improvement of music teaching quality.