Statistical learning theory （SLT） is a small-sample statistics theory. Support vector machine （SVM） is a new machine learning method based on statistical learning theory. It can process the high nonlinear problems with classification and regression, SVM not only can solve some problems, such as small-sampler over-fitting, high-dimension and local minimum, but also has higher generalization （forecasting） ability than that of the artificial neural networks. In this paper, the classification and regression methods of SVM are introduced , the characters of the methods are analyzed , and the application future of SVM in earthquake prediction is discussed also.