The soft sensor modeling method based on support vector machine (SVM) has been widely used in the field of industrial process control. However

the traditional support vector machine directly models the original measurement variables without fully extracting the intrinsic data information to improve the prediction accuracy. Aiming at this problem

a soft sensor modeling method based on deep ensemble support vector machine (DESVM) is proposed in this paper. Firstly

this method uses the deep belief network (DBN) to carry on the deep information mining

and extracts the intrinsic data characteristic. Then the ensemble learning strategy based on the Bagging algorithm is introduced to construct the ensemble support vector machine model based on the deep data characteristic

which can enhance generalization ability of soft measurement prediction model. Finally

the applications on a numerical system and real industrial data are used to validate the proposed method. The results show that the proposed method can effectively improve the prediction accuracy of the soft vector model of support vector machine and can predict the change of process quality index better.