Aspect-Based Sentiment Analysis （ABSA） tasks aim to determine the sentiment polarity of specific aspect words in comments. In the field of ABSA， dual-channel models that extract both grammar and semantic information have achieved certain results. However， the existing models fail to consider the different degrees of importance among grammar nodes， the additional noise introduced by global attention mechanism， and the existence of correlations between similar features comprehensively. To address these issues， a dual-channel graph convolutional model with dependency type and distance enhancements was proposed. Firstly， dependency types were introduced in the grammar module to measure the importance of neighborhood nodes. Secondly， mask matrices based on the dependency tree distance were constructed to filter out grammar unrelated noise. Finally， a supervised contrastive loss was introduced to facilitate the model to learn correlations between similar features. Experimental results show that on SemEval-2014 Restaurant， SemEval-2014 Laptop and Twitter datasets， compared to the second-best model DGNN （Dual Graph Neural Network）， the proposed model achieves accuracy improvements of 0.11， 0.94， 1.01 percentage points， respectively， and Macro-F1 improvements of 0.63， 1.66， 0.83 percentage points， respectively， verifying the effectiveness of the proposed model.