Aspect-Based Sentiment Analysis （ABSA） is a fine-grained sentiment analysis task aiming to analyze sentiment polarity of specific aspect words in a given text. Existing ABSA methods use Graph Convolutional Network （GCN） to process syntactic and semantic information， but they treat all syntactic dependencies of aspect words equally， ignoring the impact of distant unrelated words on target aspect words， resulting in inappropriate weight allocation of target aspect words and viewpoint words， and insufficient extraction of semantic information. Aiming at these issues， an ABSA model integrating syntax and sentiment knowledge was proposed. Firstly， a reachability matrix was constructed according to syntactic information. Based on this， a syntactic enhancement graph was constructed by weighting the central position through the aspect words. Secondly， a semantic enhancement graph was constructed by external emotional knowledge and aspect enhancement， and graph convolution was used to fully model the syntactic enhancement graph and semantic enhancement graph， respectively， so as to form different feature channels. Thirdly， biaffine attention was used to integrate syntactic and semantic information effectively. Finally， average-pooling and concatenation operations were used to obtain the final feature vectors corresponding to the aspect words. Experimental results indicate that compared with the deep dependency aware graph convolutional network model — DA-GCN-BERT （deep Dependency Aware GCN+BERT（Bidirectional Encoder Representations from Transformers））， the proposed model achieves the accuracy improvements of 1.71， 1.41， 1.27， 0.17， and 0.43 percentage points on five publicly available datasets， respectively. It can be seen that the proposed model has strong applicability in the ABSA field.