In response to several issues related to the interference of multiple aspect words in the syntactic dependency tree， redundant information caused by invalid words and punctuation marks， as well as weak correlations between aspect words and corresponding sentiment words， an aspect-level sentiment analysis model combining Strong Association Dependencies and Concise Syntax （SADCS） was proposed. Firstly， a sentiment Part-Of-Speech （POS） list was constructed to enhance the association between aspect words and corresponding sentiments. Then， a joint list incorporating POS list and dependency relationships was constructed to eliminate redundant information of invalid words and punctuation marks in the optimized dependency tree. Next， optimized dependency tree was combined with a Graph ATtention network （GAT） to model and extract contextual features. Finally， contextual feature information and the feature information of dependency relationship types were learned and fused to enhance the feature representation， enabling the classifier to efficiently predict the sentiment polarity of each aspect word. The proposed model was experimentally analyzed on four public datasets. Compared with the DMF-GAT-BERT （Dynamic Multichannel Fusion mechanism based on the GAT and BERT （Bidirectional Encoder Representations from Transformers）） model， the accuracy of the proposed model increased by 1.48， 1.81， 0.09 and 0.44 percentage points， respectively. Experimental results demonstrate that the proposed model effectively enhances the association between aspect words and sentiment words， resulting in more accurate prediction of aspect word sentiment polarity.