In the era of information explosion, personalized recommendation systems have become indispensable tools for filtering relevant content for users. However, their performance is limited by challenges including the cold-start problem, sparse data, and difficulty in capturing complex user-item relationships. This research proposes HybridGCN-Ext, a novel deep learning approach that combines Graph Convolutional Networks (GCNs) with knowledge graphs and clustering techniques to address these limitations. Unlike traditional methods relying solely on collaborative or content-based filtering, our model leverages three distinct information sources: (1) user-item interaction patterns through a simplified LightGCN architecture, (2) semantic relationships between items through Knowledge Graph Convolutional Networks (KGCN), and (3) cluster-based information through attention mechanisms to enhance item representations. Experimental results demonstrate significant improvements across recommendation quality metrics. Our findings contribute to the recommendation systems field by demonstrating how structural knowledge and clustering can be effectively combined with graph neural networks to generate more accurate, diverse, and interpretable recommendations.