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Abstract In order to solve the problem that the accuracy of aspect-based sentiment analysis method is difficult to achieve practical application, an A-LSGCN model that integrates attention mechanism and considers sentence syntactic structure and corpus co-occurrence information is designed, to improve the accuracy ,when predicting the sentiment polarity of each specific attribute in a given sentence. Firstly, combined the multi-head attention mechanism and the lexical-syntactic graph convolution for the superimposing of the attribute memory vector and the historical context memory vector, to obtain the relationship between the target attribute word and its contextual content. Secondly, in order to reduce the redundancy of the classification interference, and learn the general grammar knowledge enough, the syntactic dependency graph neural network is used to extract the syntactic structure information, directly match the attributes and their emotional expressions, and finally obtain the emotional polarity corresponding to the specific attributes through network classification calculation. In a comparative experiment on multiple SemEval datasets, the MF1 score and accuracy of the Laptop14 dataset improved by 1.1% and 5.5%, respectively.