A landslide displacement prediction model is proposed based on CRITIC (Criterion Importance Through Intercriteria Correlation) weighting method and particle swarm optimization (PSO) algorithm to optimize support vector regression (SVR) in order to address the issue of no difference in input weight values in landslide displacement prediction, which affects the accuracy of the model prediction. The model first uses Pearson correlation analysis to select three influencing factors with strong correlation with the model output. Then, the CRITIC weighting method is used to obtain the corresponding weights, and the weighted training set is 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.