为提高滑坡长期位移预测精度，将GCformer模型运用到滑坡位移预测，提出一种基于GCformer模型的滑坡位移预测方法。该方法以降雨量和位移量作为输入，利用GConv_msk模块获取序列的全局信息，并结合序列长度线性缩放来高效提取数据特征。同时，利用PatchTST模型来自动提取序列数据中的近短期信号，获取更全面的历史信息，从而提高模型的鲁棒性和对数据的建模能力。最后，利用甘肃定西市会川镇锦柳坪村和元始滩村滑坡位移监测数据进行实例验证。结果表明：该模型与Autoformer模型和FEDformer模型相比，GCformer模型在总位移和垂直位移上具有更高的预测精度和更好的可靠性。

To enhance the long-term displacement prediction accuracy of landslides, the GCformer model was applied to landslide displacement forecasting, and a novel landslide displacement prediction approach grounded in the GCformer model was proposed. This methodology leveraged rainfall and displacement as input variables, utilized the GConv_msk module to capture the global information of the sequence, and combined a linear scaling technique of sequence length to efficiently extract data features. Concurrently, the PatchTST model was employed to automatically extract short-term and long-term signals from the sequence data, in order to obtain more comprehensive historical information and bolster the model's robustness and modeling capability. Finally, the landslide displacement monitoring data from Jinliuping Village and Yuanshitan Village in Huichuan County, Dingxi City, Gansu Province, were utilized for case validation. The findings demonstrate that the proposed model exhibits superior prediction accuracy and reliability. In comparison to the Autoformer model and the FEDformer model, the GCformer model is found to achieve the lowest error in both total displacement and vertical displacement.