To solve the problem of low diagnosis rate of multi-type faults in industrial processes, a method of boundary discriminant projection (MDP) and support vector machine (SVM) fusion (MDP-SVM) was proposed. Boundary discriminant projection is often used in the field of face recognition, which can reduce the dimensionality of multiple types of data to obtain clear boundaries of different categories. Compared with principal component analysis (PCA) and local linear embedding (LLE), the local and global structures of samples are considered and the problem of small samples is avoided. The classification of dimensionality reduction data is judged by SVM classifier, and the optimal SVM classifier is obtained by particle swarm optimization (PSO) algorithm to achieve fault diagnosis. The simulation results show that compared with the traditional method, the fault identification accuracy of the proposed method can reach 95.379%, and multiple faults can be identified simultaneously.