This study proposes a multimodal music teaching analysis and intelligent evaluation system based on neural network, which aims to use multimodal data fusion and deep learning technology to intelligently and objectively evaluate learning performance in the music teaching process. By combining data from three modalities, audio, video and text, the system realizes the feature extraction and optimization fusion of multi-source information, effectively improving the accuracy and stability of the evaluation. The intelligent evaluation model structure designed in the study shows good convergence and generalization ability, which can stably output evaluation results and overcome the influence of subjective factors. The experimental results show that the integration of multimodal information makes up for the shortcomings of single modal data, forms a more comprehensive learning performance analysis, and provides strong support for music teaching. The visualization function of the system makes it easy for teachers to intuitively obtain evaluation feedback, thereby further improving teaching strategies. The research not only realizes the standardization and intelligence of music teaching evaluation, but also provides new ideas for future research on multimodal music teaching evaluation.