This study utilizes artificial intelligence technology to design a music education model based on pattern recognition. Music signals are preprocessed to extract emotional feature vectors from the signals. A music emotion recognition mapping framework is established, and a backpropagation (BP) neural network is employed for automatic music emotion recognition. Combining emotional teaching measures in music education, the application scheme for the emotion recognition teaching model is determined. Through teaching practice, the effectiveness of the proposed scheme in improving music education is evaluated. 96% of students believe that the model-assisted approach is helpful for classroom learning, and 87% of students actively use the model for learning after class. All students have improved their interest and confidence in music learning through teaching practice. Regarding students’ attitudes toward teaching methods, 90% of students approve of the AI-assisted teaching model, and after learning, 96.5% of students feel that learning music is an enjoyable experience.
