The invention provides an SVM (Support Vector Machine)-based Web partitioning method, which comprises the steps of: partitioning all Websites into N groups; taking K=1, 2, 3, ......,N, for the value of each K, selecting Website samples in the 1st-(K-1)th, (K+1)th-Nth groups, and initializing LibSvm training; training LibSvm; storing a trained SVM model; selecting a Website sample in a Kth group to perform a Web partitioning test; and saving the Web partitioning test result. According to the method provided by the invention, the generalization capability of the SVM is strong, fault tolerance and classification can be better performed when louder noise data are processed. The accuracy rate of coordinates established by a network coordinate system is approximately 80 percent, the problem of nonlinear classification can be solved by the SVM, the number of classification by the SVM is fixed, the extreme condition that no crawler crawls on a website is avoided, and the uncertainty of partitioning the number of a set in the clustering algorithm is overcome by using the classification algorithm.