Aiming at the single information problem of traditional sentiment classification method, a multi-head attention model with user and product information is proposed. Firstly, hierarchical multi-head attention is used to replace single-head attention and obtain effective information from multiple perspectives. Secondly, using multi-head attention with user information and product information, mining the performance characteristics of user and product information in multiple subspaces, the model can get a more global impact of user preferences and product characteristics on sentiment score in multiple subspaces. Experimental results on IMDB, Yelp2013, and Yelp2014 datasets show that the performance of the proposed model is better than the other advanced baselines.