In order to address the issue of no difference in input weight values in landslide displacement prediction, which affected the accuracy of the model prediction, a landslide displacement prediction model was proposed based on CRITIC (criterion importance through intercriteria correlation) weighting method and particle swarm optimization (PSO) algorithm to optimize support vector regression (SVR). The model first used Pearson correlation analysis to select three influencing factors with strong correlation with the model output. Then, the CRITIC weighting method was used to obtain the corresponding weights, and the weighted training set was input into a prediction model based on CRITIC weighting method and PSO-SVR to achieve the prediction of landslide surface displacement. The results show that compared with SVR, PSO-SVR, and prediction models based on entropy weighting and PSO-SVR, this model has good generalization ability. The root mean square error and decision coefficient are reduced by 38.24% and increased by 6.64% compared to the unweighted model, respectively, which can effectively improve prediction accuracy. The prediction effect is better than other comparison models.