Abstract In order to establish a high-precision slope displacement prediction model, we use phase space reconstruction(PSR) to transform the slope displacement time series data into multi-dimensional data. The wavelet kernel function is constructed to improve the support vector machine model and to establish the PSR-WSVM model. The model is applied to slope displacement prediction. The PSR-WSVM model prediction results are compared with the traditional support vector machine model(SVM), wavelet support vector machine model(WSVM) and phase space reconstruction-based support vector machine model(PSR-SVM) prediction results. The average absolute error is passed. Mean absolute error percentage(MAPE) and root mean square error(RMSE) accuracy evaluation indicators verify the feasibility of the PSR-WSVM model. The results of engineering examples show that the three precision evaluation indexes of PSR-WSVM model prediction result are better than the other three models, and the accuracy of slope displacement prediction has obvious improvement.