In view of the problems that aspect level sentiment analysis tasks can not give full consideration to syntactic comprehensiveness and semantic relevance, and the graph volume used in most studies only considers the top-down dissemination of information and ignores the bottom-up aggregation of information, this paper proposes an sentiment analysis model based on attention and dual channel network. While expanding the dependency representation, the model uses self attention to obtain the information matrix with semantic relevance, and uses a dual channel network to combine comprehensive syntactic and semantic relevance information. The dual channel network focuses on the semantic features of top-down propagation and the structural features of bottom-up aggregation respectively. The graph convolution output in the channel will interact with the information matrix, pay attention to complement the residual, and then complete the tasks in the channel through average pooling. Finally, the final emotion classification features are obtained by the fusion of semantic based and structure based decision-making. The experimental results show that the accuracy and F1 value of the model are improved on three public data sets.