A news event evolution analysis and situation prediction model integrating knowledge graphs and spatio-temporal graph convolutional networks

Existing models for news event evolution analysis and situation prediction struggle to balance event semantic dynamics and spatio-temporal feature complexity.Knowledge graphs' static properties cannot capture spatio-temporal evolution patterns, and conventional ST-GCN's insufficient semantic fusion limits prediction accuracy.This study proposes a model integrating DE-KG and semantic-aware ST-GCN; its dual-modal fusion module achieves deep semantic-spatio-temporal feature coupling, improving event evolution analysis and situation prediction.Experiments show the model's evolution segmentation F1-score of 0.850 (+20.6% vs. ST-GCN) and situation prediction RMSE of 0.089 (+63.7% vs. ARIMA, lower decay rate), with an optimal RMSE of 0.083 for public health events.Results verify that DE-KG's semantic dynamics fix static graphs' spatio-temporal gaps, the semantic-aware matrix adapts ST-GCN topology to event semantics, and dual-modal fusion strengthens feature complementarity -greatly improving complex event analysis and prediction performance.