Accurately predicting landslide deformation based on monitoring data is key to successful early warning of landslide disasters. Landslide displacement–time curves offer an intuitive reflection of the landslide motion process and deformation predictions often reference the Saito curve for correlational analysis with cumulative deformation curves. Many scholars have applied machine learning techniques to individual landslide deformation predictions with considerable success. However, most landslide monitoring data lack a full lifecycle, making it challenging to predict unexperienced evolutionary stages. Cross-learning between similar landslide datasets provides a potential solution to issues of data scarcity and accurate prediction. First, this paper proposes a landslide classification and displacement machine learning method, along with predictive performance evaluation metrics. Further, it details a study of 13 landslides with evident deformation signs in the middle Qinling–Bashan Mountains area, conducting refined landslide classification. Based on a data-driven approach, this study conducts an analysis of the importance of characteristics influencing landslide deformation and establishes predictive models for similar-type landslide deformation, mixed-type landslide deformation, and individual landslide deformation using machine learning algorithms. The models trained on the dataset are used to predict the deformation of the West of Yinpo Yard landslide at different periods, with the predictive performance evaluated using two indices. The results indicate that the models trained on similar-type landslide data and those based on individual landslide data yielded comparable predictive performances, substantially addressing challenges such as insufficient early-stage monitoring data and low prediction accuracy.

The displacement–time curve of a landslide serves as a critical indicator of its movement state, with precise deformation prediction being essential for effective disaster early warning. While numerous studies have employed machine learning techniques to predict deformation at individual monitoring points, they often overlook the spatial correlations among monitoring points arranged along horizontal and vertical cross-sections. To address this limitation, this paper employs the Temporal Graph Convolutional Network (T-GCN) model, which integrates the strengths of Graph Convolutional Networks (GCNs) and Gated Recurrent Units (GRUs). The GCN captured spatial correlations among monitoring points, while the GRU modeled the temporal dynamics of displacement. The T-GCN model was applied to predict the spatio-temporal deformation of the Dawuchang landslide in the Three Gorges Reservoir area. Experimental results demonstrated that the T-GCN model effectively predicted the spatio-temporal displacement of landslides, offering a robust approach for landslide monitoring and early warning systems. The model also incorporated the temporal influence of external factors, such as rainfall and reservoir water levels, enhancing prediction accuracy and providing valuable insights for future research in landslide deformation forecasting.