Aerobics is full of charm, and music plays an inestimable role in it. With the penetration of music in aerobics, the “sound” of music art is introduced into the “shape” of aerobics movements, and the visual art and visual experience are perfectly combined, which greatly expands the extension and extension of aerobics. This paper proposes an aerobics music adaptation recommendation algorithm that combines classification and collaborative filtering. First, by calculating the similarity of the user context information, the collaborative filtering algorithm obtains the initial annihilation grass music recommendation list; then the classification model is trained by the machine learning algorithm to obtain the user’s aerobics music type preference in a specific context; finally, collaborative filtering The obtained recommendation list is integrated with the aerobics music preference obtained by the classification model to provide personalized aerobics music adaptation recommendations for users in specific situations. In the specific aerobics music adaptation recommendation, the algorithm is implemented by a deep neural network composed of an independent cyclic neural network algorithm and an attention mechanism. In the data preprocessing stage, the audio of the user’s listening history is preprocessed by scattering transformation. The audio features of the user’s listening history are extracted by scattering transformation, and then this feature is combined with the user’s portrait to obtain a recommendation list through an independent recurrent neural network with a hybrid attention mechanism. The experimental results show that this method can effectively improve the performance of the personalized music recommendation system. Compared with the traditional single algorithm IndRNN and LSTM, the recommendation accuracy is improved by 7.8% and 20.9%, respectively.
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