Most of the current aspect sentiment triplet extraction methods do not fully consider syntactic structure and semantic relevance. This study proposes an aspect sentiment triplet extraction model that combines syntactic structure and semantic information. First, the study proposes to construct a grammatical graph with a dependency parser to get the probability matrices of all dependency arcs, extracting rich information of syntactic structure. Second, it utilizes the self-attention mechanism to construct a semantic graph, which represents the semantic correlation between words, thus reducing the interference of noisy words. Finally, a mutual affine transformation layer is designed to allow the model to better exchange the relevant features between the syntactic graph and semantic graph to improve the performance of the model in sentiment triplet extraction. The model is validated on several public datasets. The experiments show that compared with the existing sentiment triplet extraction models, the precision (P), recall (R), and F1 value are all improved. This validates the effectiveness of combining syntactic structure and semantic information in aspect sentiment triplet extraction.