Existing aspect-based sentiment analysis methods do not use enough information of syntactic dependency trees， ignore the associations between multiple aspect words， and lack the use of external knowledge. Aiming at these problems， a Knowledge Enhanced Aspect word Interactive Graph neural network （KEAIG） model was proposed. Firstly， BERT-PT （Bidirectional Encoder Representation from Transformers with Post-Train） fused with domain knowledge was used to encode text， and the knowledge graph was used to add sentiment information to the syntactic trees. The information contained in the syntactic dependency tree was extracted by the model in two parts： in the first part， the association relationships in the syntactic dependency tree and the part-of-speech tag of each word were used to extract sentence features， and in the second part， the feature extraction was performed on the syntactic dependency tree combined with the knowledge graph. Afterwards， the fusion gated unit was used to fuse the association features of multiple aspect words. Finally， the two parts of the sentence representations were concatenated together as the final classification basis. Experimental results on four datasets show that compared with the benchmark model Relational Graph Attention Network （RGAT）， the proposed model improves the accuracy by 2.17%， 5.54%， 2.60%， and 2.83%， respectively， and the F1 score （Macro?F1） by 2.69% and 6.87%， 8.77%， and 14.70%， respectively， fully demonstrating the effectiveness of using syntactic trees， introducing external knowledge and extracting multi-aspect word associations.