Aspect level sentiment analysis is a more fine-grained sub task of sentiment analysis tasks, the purpose of which is to predict sentiment tendencies of a certain aspect. At present, most aspect level sentiment analysis tasks use neural networks to extract semantic information of sentences, and then predict emotional polarity. Based on this, this study proposes a semantic representation method based on sentence structure information, that is, the fusion of sentence structure information in the part of speech sequence of the statement. In this work, two Bi-LSTM are used to extract the semantic feature and the structural feature of the statement, and the semantic representation based on sentence structure is constructed. Then, the given aspect level vectorization is embedded into the semantic representation based on the sentence structure, and then sent to the Softmax layer for sentiment classification. Experiments show that the semantic representation method based on the information of sentence structure is more effective.