Graph Convolutional Networks （GCNs） have demonstrated significant potential in graph representation learning. However， existing methods still exhibit limitations in learning global topological relationships and fusing topological structure with attribute features. To address these challenges， a Graph Convolutional Network enhanced by Graph Diffusion and Dual-View feature learning （GCN-GDDV） was proposed. Firstly， a generalized graph diffusion mechanism was introduced to construct diffusion graphs containing global topological structure information. Then these diffusion graphs were combined with attribute-feature-based K-Nearest Neighbor （KNN） graphs to perform dual-view feature learning via GCN， capturing relationship dependencies in the global structure and the semantic similarities of node attributes， respectively. Finally， an attention network was designed to adaptively fuse topological structures and attribute features. Node classification experimental results on three benchmark graph datasets demonstrate that GCN-GDDV outperforms the suboptimal method， achieving average improvements of 1.78%， 1.60%， and 0.30% in accuracy， Macro-F1， and Micro-F1 metrics， respectively.