Aspect-Based Sentiment Analysis （ABSA） aims to infer the sentiment polarity towards specific aspects. The existing methods often adopt an equal-weight strategy when aggregating neighbor node information， failing to distinguish the differences in node importance， which leads to insufficient semantic-syntactic coordination. Graph ATtention network （GAT） relies on indirect syntactic associations to capture the relationships between aspect words and sentiment words， resulting in a lack of global features and low distinguishability in the sentiment representation space， thereby causing ambiguous sentiment space. To address the above issues， a graph convolutional network model integrating Mutual Information （MI） alignment and supervised contrastive learning， MIC-GCN （Mutual Information and Contrastive learning-based Graph Convolutional Network）， was proposed. First， text words were converted into vector representations through an embedding layer. Second， a dual-attention dynamic routing module was designed to combine the dynamic interaction of self-attention and aspect-aware attention， so as to generate an attention score matrix， and a dynamic masking matrix was constructed on the basis of syntactic distance， so as to optimize the utilization of structural information. Third， the MIC-GCN module was used to construct the original graph and pseudo-graph. After extracting local features through convolution， local and global features were aligned by MI maximization. Finally， the supervised contrastive learning was introduced to optimize the sentiment representation space and enhance the ability to distinguish sentiment polarity， so as to complete sentiment polarity judgment through pooling and classification layers. Experimental results demonstrate that on three public datasets — Twitter， Lap， and Rest， the proposed model has the accuracy 0.10， 1.71， 0.30 percentage points higher than the best baseline models SSEGCN （Syntactic and Semantic Enhanced Graph Convolutional Network）， T-GCN（Type-aware Graph Convolutional Network）， and DGEDT （Dependency Graph Enhanced Dual-Transformer network）， respectively. It can be seen that the proposed model enhances semantic-syntactic coordination effectively， improves the efficiency of capturing global features and the distinguishability of sentiment polarity， demonstrating excellent performance in ABSA tasks.