Temperature Prediction for Chinese Provinces Based on Graph Convolutional Network

Temperature prediction, as a critical topic in meteorology, represents an important application direction for artificial intelligence, data science, and information processing. This paper proposes a regional temperature prediction method based on graph convolutional networks (GCN), which not only considers historical temperature variations but also incorporates inter-regional interactions and influences, thereby constructing a more accurate predictive model. Experiments demonstrate that the proposed method outperforms traditional approaches in long-term forecasting.