The kernel entropy component analysis(KECA) feature extraction process only retains the maximum Renyi entropy information of the data, but does not fully utilize the category information of the data. As a supervised learning algorithm, linear discriminant analysis(LDA)can effectively extract category information in features. Therefore, a feature extraction method of KECA-LDA (KEDA) was proposed. Firstly, KECA reduced the dimension of the data according to the minimum Renyi entropy loss strategy. Then, the LDA algorithm was used in the KECA feature space to obtain low-dimensional features with discriminative information and input them into the support vector machine(SVM)classifier. The best performance SVM was obtained by beetle antennae search(BAS)and to build a fault diagnosis model. The KEDA-BAS-SVM method was applied to the Tennessee eastman(TE) for simulation experiments. The results showed that When the matrix similarity optimization based on distance measure was used to determine that the kernel parameter of RBF selected in KECA, compared with the KECA and LDA algorithms, after using KEDA feature extraction, the accuracy of multi-type fault diagnosis reached 99.7%, which verified the superiority of KEDA-BAS-SVM in the field of multi-type fault diagnosis.