Aiming at the problem that landslide displacement is highly nonlinear and complex, and it is difficult to use traditional optimization algorithms combined with artificial intelligence methods for more reasonable and accurate predictive modeling, a L-vy flight strategy chaotic sparrow optimization algorithm (CLSSA)-variable modal decomposition (VMD)-support vector regression (SVR) landslide displacement prediction model is proposed. Firstly, CLSSA is used to optimize the VMD decomposition parameters to decompose the landslide displacement time series, secondly, the CLSSA-SVR model is used to predict the VMD decomposition subsequence, and finally, the cumulative displacement prediction is derived by superimposing the subsequence prediction data. The model is validated by taking the Baishui River landslide as an example, and the experimental results show that the proposed method has an MAE of 2.24 mm, an RMSE of 3.37 mm, and an R2 of 0.995 in the final cumulative displacement prediction, and relative to the sparrow optimization algorithm-variable modal decomposition-support vector regression (SSA-VMD-SVR), the improved optimization algorithm increases the adaptive ability of VMD that improves the efficiency of landslide displacement prediction for each component.