Aspect-Based Sentiment Analysis （ABSA） is an important task in natural language processing. The existing methods generally adopt single-type nodes to model semantic associations between aspects and opinion terms， failing to distinguish the contributions of different node types to sentiment representation effectively. Moreover， current models usually construct syntactic trees using dependency parsers for semantic analysis， ignoring fine-grained semantic information of the word itself and implicit sentiment intensity modification relations in the text. To address the above issues， a Heterogeneous enhancement and Multi-Source knowledge fusion Graph Convolutional Network （HMS-GCN） model was proposed. First， a heterogeneous graph with differentiated constraints was constructed to capture semantic and sentiment information in sentences multi-dimensionally by using the relations of multi-type nodes and their corresponding edges in the heterogeneous graph， and importance of different types of nodes and their internal components were focused on by utilizing a dual-channel attention mechanism. Second， sememe knowledge was adopted to enhance the semantic representation of initial textual features， and hierarchical phrase structural information was integrated on the basis of the syntactic adjacency matrix， so as to improve the model’s syntactic dependency comprehension. Finally， the heterogeneous graph network information and enhanced knowledge features were fused for sentiment classification prediction. Experimental results on public datasets （Twitter， Lap14， Rest15， Rest16） show that compared to models such as ISSK-GCN （Integrating Syntax and Sentiment Knowledge Graph Convolutional Network）， the proposed model achieves the best results， validating the effectiveness of the differentiated node modeling and knowledge enhancement strategies.