To address the issues of structural unfairness and classification inaccuracy of Graph ATtention network （GAT） model in node classification tasks， a Graph Neural Network （GNN） node classification model incorporating clustering coefficients， named GATcc（GAT with clustering coefficient）， was proposed. Firstly， by introducing the clustering coefficients of neighboring nodes as structural information， and combining trainable weight parameters， the representation ability of the topological structure in the attention mechanism was enhanced. Then， feature scaling was employed to optimize node embeddings， and residual connections were added to mitigate the risk of feature over-smoothing. Experimental results on six real datasets demonstrate that the proposed model outperforms the mainstream models， such as Graph Isomorphism Network （GIN） and GOAT （Graph Ordering Attention Network）， in classification accuracy. For instance， compared to the baseline model GAT on the Cora dataset， the proposed model has the classification accuracy improved by 4.03 percentage points， the structural bias reduced from 0.31% to 0.11%， and the classification accuracy of isolated nodes improved by 3.69 percentage points. In conclusion， the proposed model not only achieves significant improvements in classification performance， but also shows superiority in structural fairness and stability.