Urban surveillance systems are inherently limited in their ability to monitor complex crowd dynamics due to the restricted coverage of single-camera setups. This study proposes a novel Spatial-Temporal Graph Convolutional Network framework for predicting abnormal crowd aggregation. Our method introduces a composite anomaly aggregation metric that synthesizes three critical factors: the spatial distribution of abnormal groups (core anomaly intensity), ambient pedestrian flow variations (environmental sensitivity), and suppression mechanisms for regular large-scale gatherings. By constructing topological graphs based on camera networks and performing spatio-temporal convolution operations, the model effectively integrates multi-view information to identify latent risk areas. Combining the camera topology structure and the spatio-temporal graph convolutional network, this method can accurately predict abnormal aggregation points in the spatial and temporal dimensions, and effectively identify potential abnormal risk areas through multi-camera information fusion.