Text matching is a core research area in natural language processing. Deep text matching models can be broadly classified into representational models and interactive models. The former tends to lose semantic focus and fails to measure the contextual importance of words. The latter lacks global information such as sentence type and inter-sentence information. To address these problems, we propose a text matching model incorporating multi-angle features based on Siamese neural network. The model generates word vectors using the BERT model and enhances semantic features by the similarity fusion of words. It then encodes the syntactic structured features using Bi-LSTM, namely the syntactic structured information containing the lexical sequence. A Transformer encoder is utilized to realize the multi-level interaction between the syntactic structured features and the text features. Finally, the similarity is deduced by spliced vectors. Experiments on part of Quora question pair show that this model performs better than the classical deep matching model.