Abstract: Due to the combined influence of complex engineering geological conditions and environmental factors, the evolution of slope deformation is complicated and nonlinear. As support vector machine (SVM) could effectively solve the small sample, high dimension, and nonlinear problems, it is employed for the data mining of measured displacements of slope and the forecasting of slope deformation trend. In order to avoid the blindness of human choice of SVM parameters and to improve the prediction accuracy and generalization ability of the model, ACO-SVM model is built by adopting improved ant colony algorithm(ACO) to optimize parameters in association with rolling forecasting method of displacement time-series. The model is applied to two engineering examples. The research results show that ACO-SVM model is correct with high accuracy. Compared with optimizing SVM based on genetic algorithm or particle swarm optimization, ACO-SVM model has higher accuracy of prediction and stronger generalization ability. The forecasting results are more reasonable. It is of practical value for slope deformation prediction.