As one of the classic research directions in the field of natural language processing, the task of target-specific sentiment analysis is to determine the sentiment polarity of a specific target based on contexts. The key to improve the performance of this task is how to better mine the semantic representation of specific target and contexts. This study proposes a multi-attention network with phrase features. By introducing phrase-level semantic features, a multi-attention network with multi-granularity features is constructed to improve the expression ability of the model effectively. The experimental results on the SemEval2014 Task4 Laptop and Restaurant datasets show that the PEMAN model proposed in this study has a certain improvement in accuracy compared with the benchmark model.