Traditional models based on recurrent neural networks have insufficient predictive capabilities for stepwise landslide displacement.To address this issue,a dynamic landslide displacement prediction model based on time series analysis and CNN-BiLSTM-AM is proposed.Firstly,the sequence is decomposed into trend components,periodic components,and random components using the variational mode decomposition(VMD)method.The trend component displacement is fitted using the second-order exponential smoothing method.Then,the maximum mutual information coefficient(MIC)method is introduced to calculate the correlation between various influencing factors and periodic component displacement.For both periodic and random component displacements,a hybrid CNN-BiLSTM-AM model is used for multi-factor and single-factor prediction.Finally,the predicted values of each component are accumulated to obtain the cumulative displacement prediction results.Experimental results show that the proposed method achieves fitting coefficients R²of 0.984 and 0.987 in the final cumulative displacement prediction results,with average absolute errors(MAE)of 5.334 and 3.947,and root mean square errors(RMSE)of 6.196 and 4.941,respectively.Compared to CNN-LSTM,SSA-KELM,and NARX methods,the proposed method better captures the temporal correlations in monitoring data,significantly improving prediction accuracy,and providing valuable references for stepwise landslide early warning and mitigation efforts.