Abstract: Aiming at the problems of low classification accuracy,long period,and difficulty in meeting the demand for theme music in people’s life,an attention mechanism and LSTM (Long Short-Term Memory) were designed.Based on the neural network model,it consists of a music theme model and a music recommendation model.On the basis of using the attention mechanism and the LSTM network to realize music emotion classification,the music theme model effectively combines the audio codebook and the topic model to achieve Discrimination of a subcategory of music topics under an emotion.In the music recommendation model,a low-level descriptor and a spectrogram are used to construct a joint representation of manual features and Convolutional Recurrent Neural Network (CRNN) features.The emotions expressed by the user’s voice are obtained,and the user is given a precise music theme recommendation by using this mo-del.In the experiment,two models were designed separately,and two different traditional models were used as the baseline.The experimental results show thatthis model not only can improve the classification accuracy of the subject,but also can accurately judge the emotion of the user’s voice data,so as to achieve the recommendation of the theme music compared with the traditional single model.

Key words: Attention mechanism, Convolutional recurrent neural network, Long short-term memory network, Low-Level descriptor, Music theme recommendation, Topic model