Although the click farming on e-commerce platform improves the store profits to some extent, but it raises the promotion cost of e-commerce platform, which leads to a serious problem of reputation security, and on the other hand, it misleads consumers with property loss. To solve these problems, an intelligent method named SVM-NB was proposed for detecting the click farming on e-commerce platform for users, and a method of constructing characteristics of click farming was also put forward. Firstly, the relevant data of commodity were collected to create an eigenvalue database. Then a classifier was established based on Support Vector Machine (SVM) algorithm with supervised learning, so as to judge the result of click farming. Finally, the click farming probability of goods was calculated by using Naive Bayes (NB), which can provides users with a reference for their shopping. The reasonality and accuracy of the proposed SVM-NB method was validated by K-fold cross validation algorithm, and the accuracy reached 95.0536%.