Landslide displacement prediction is an important task in disaster prevention and mitigation. Aiming at the rationality problem of trend term and period term reconstruction after displacement decomposition as well as the problem of low accuracy of period term displacement prediction, a combined model of improved adaptive noise complete ensemble empirical modal decomposition (ICEEMDAN), sample entropy (SE), and dung beetle optimization algorithm (DBO) optimization of the long- and short-term memory network (LSTM) is presented displacement prediction is performed. Taking the Bazimen landslide as the research object, the cumulative displacement of the landslide was decomposed using the ICEEMDAN method, and the subsequence obtained from the decomposition was characterized by the sample entropy value, which was reconstructed into the trend term and the period term displacements. After that, the LSTM model is used to predict the trend term and the period term displacements. The influence factors of the period term displacement are determined by the method of gray correlation. Considering that the randomness of hyperparameters in the LSTM network affects the model prediction accuracy, the dung beetle optimization algorithm is introduced to obtain the optimal hyperparameters of the LSTM, and finally the predicted trend term and period term displacements are superimposed to obtain the cumulative displacement. The ICEEMDAN-SE-DBO-LSTM model proposed in this paper predicts the period term displacement with the RMSE, MAE and R2 of 1.803 mm, 1.584 mm and 0.988, respectively, which is better than the DBO-BP, LSTM, GRU and BP models, and proves the effectiveness of the model.