Aspect-Based Sentiment Analysis (ABSA) focuses on determining the sentiment polarity of specific aspect terms within sentences. Previous methods have achieved remarkable performance by leveraging graph neural networks and attention mechanisms to learn dependency tree structures. However, during the graph learning process in complex global structures, irrelevant contextual information is often introduced, and reliance on single-graph structural features may lead to inaccurate predictions. To address these issues, we propose ACAGCN, a novel framework designed to enhance context relevance and representation diversity. ACAGCN incorporates a context-aware semantic learning module, which effectively filters out irrelevant context while strengthening semantic correlations related to specific aspects. It adopts a multi-channel Graph Convolutional Network (GCN) architecture, consisting of AspGCN, SemGCN, and SynGCN, to capture aspect-related information, semantic associations, and syntactic structures, respectively. Additionally, we introduce an adaptive aggregation network to facilitate the fusion of the three GCN channels, achieving the integrity of different structural representations. Experiments on three benchmark datasets demonstrate the effectiveness of our framework.