Abstract: Users' long-term preferences and the relationship between historical information and current situation are usually ignored in existing user preference modeling and user record unified modeling. To address this, in this paper, we propose a music recommendation model based on users' long-term and short-term preferences and music emotional attention. Firstly, we divide the user's listening record into multiple historical and current sequences, and learn the features by multiple long and short-term memory networks, respectively, to obtain user's long and short-term preferences. For the historical music sequences, we propose the concept of sequence period is proposed to obtain the long-term preferences by calculating the weights of the sequence period. For the current sequence, we use the average pooling to extract the music features of the current scene to obtain the short-term preference. Secondly, we learn the emotional characteristics of music from the acoustic signals, and use the attention mechanism to calculate the emotional factors of music. Finally, we integrate the music emotion factor into the user's long-term and short-term preference to generate a music recommendation list. The experimental results on the last.fm real data set show that the proposed model achieves 0.5435 in term of the NDCG@10, which is better than the existing methods. The ablation experiment and characteristic contribution analysis further demonstrate the effectiveness of the model.