Read the full-text of this article for free

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

Authors: Yingpei Xi

Addresses: School of Media and Design, Xi'an Peihua University, Xi'an, 710125, Shaanxi, China

Abstract: 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.

Keywords: news events; situation prediction; spatio-temporal evolution; spatio-temporal graph convolutional network; ST-GCN; semantic fusion.

DOI: 10.1504/IJICT.2026.152854

International Journal of Information and Communication Technology, 2026 Vol.27 No.31, pp.80 - 102

Received: 04 Dec 2025
Accepted: 12 Jan 2026
Published online: 13 Apr 2026 *