Support Vector Machines (SVM) is a new type of machine learning algorithm. Compared with conventional learning algorithms, SVM enhances the generalization ability of the models by employing structural risk minimization criterion to minimize the sample errors and simultaneously decrease the upper bound of the predict error of the models. The global optimal solution can be uniquely obtained owing to that SVM converts machine learning into quadratic programming. Based on the local data from hydrogenation equipment, a predictive model using Least Squares Support Vector Machines (LS-SVM) is established for three important quality targets of diesel oil in this paper, and compared with neural network and stands SVM on precision. Finally, it is proved that the proposed predictive models based on LS-SVM can predict the quality target more efficiently and rapidly than stands SVM and neural network. It provided a method for online diagnosing fault of quality targets.