Abstract: A slope deformation model of metal mines transferred underground mining from open-pit based on support vector machines was presented. The model can effectively express the non-linear variation of metal mine open-pit slope deformation caused by underground mining disturbance. In the model the RBF kernel function was utilized to train on-site monitoring data, the cross-validation was employed to choose model parameters, support vectors were achieved with training samples, and then the future deformation was predicted. The model was applied to Xingshan Iron Ore transferred underground mining from open-pit. The results show that the regression value of learning samples is extremely precise and the predicted deformation has a higher precision based on support vector machines. The application of the model, which predicts the deformation with the achieved support vectors, is convenient and it bears a stronger generalization ability.