Paraphrase identification involves natural language semantic understanding. Most previous methods regarded sentences as sequential structures, and used sequential neural network for semantic composition. These methods do not consider the influence of syntactic structure on semantic computation. In this paper, we proposed a neural paraphrase identification model based on syntactic structure, and designed a tree-based neural network model for semantic composition, which extended the semantic representation from word level to phrase level. Furthermore, this paper proposed a syntactic tree alignment mechanism based on phrase-level semantic representation, and extracted features by using cross-sentence attention mechanism. Finally, a self-attention mechanism was used to enhance semantic representation, which could effectively model context information based on syntactic structure. Experiments on Quora paraphrase dataset show that the performance of paraphrase identification has been improved to 89.3% accuracy. The results further prove that the proposed semantic composition method based on syntactic structure, phrase-level cross sentence attention and self-attention are effective in improving paraphrase identification.