The effect of data-driven deep learning structural damage identification （SDI） is greatly affected by factors such as structural complexity， model construction method and data size， etc. We introduce graph convolutional neural network （GCN） to integrate the attribute features between structural nodes， explore the complex attribute relationships between nodes from a graph perspective， and provide multi-dimensional learning information for SDI. To this end， a graph convolutional neural network integrating multi-dimensional features of structure （S-GCN） model was designed， which integrates multidimensional structural features. Based on structural vibration data， a damage feature matrix was constructed， and through a derived graph network， the connection relationship between nodes and edges in the graph was represented. An edge index matrix was constructed， and multidimensional feature information such as structural damage status， vibration data， and node attributes was input into GCN for structural damage feature extraction and identification. The application effect of GCN in damage identification driven by multidimensional structural feature information was explored. The feasibility and effectiveness of two steel structure verification methods were verified， and the results showed that S-GCN can integrate multi-dimensional structural feature information， achieve high damage identification accuracy for both structural objects， and demonstrate good noise robustness. Further comparative analysis shows that S-GCN can efficiently update node features and predict node damage status based on inter-node relationships compared with the three non-GCN models. Its damage recognition accuracy， computational efficiency， and network layer evolution process are all better than the comparative models， verifying the effectiveness of integrating structural spatial features in structural damage recognition.