The learning ability of traditional models is reduced by copious constrained samples, so an Internet user classification model based on improved Support Vector Machine (SVM) is designed, which simulates the browsing trajectories of Internet users by constructing sample data. A brand-new user classification strategy according to user preferences is formulated. Then, Internet users are classified based on improved SVM. According to the three performance tests, the model has satisfying classification ability because its average accuracy is 98.56%, higher than the expected value. Seen from the comparative tests with two traditional user classification models, this model can maintain a high level of learning ability in the face of increasing sample data.