The invention discloses a piano teaching method based on big data and neural network, which classifies the beats of audio files, uses the training BP neural network to classify and identify different piano music genres, and adopts Thayer's two-dimensional emotion model to convert the emotion of music The types are divided into four categories: happy, relaxed, sad and angry. The digital music files are stored in different storage layers, and the differences are added with identity information tags. The large cloud database will compare the differences in beats and timbres in each segment and The corresponding music file fragments in the large cloud database are fed back to the teaching terminal. According to the scores of piano practice results of different rhythms, genres, and emotions, combined with identity information of different regions, ages, and genders, similarity evaluations of different priority levels can be carried out according to identity information. The evaluation of teaching results is more targeted and can be based on user preferences. Music content, find the music content closest to it for practice recommendation.