Aiming at the problems of low diagnostic accuracy of SVM and easy to fall into local optimum of BWO algorithm in transformer fault diagnosis, a transformer fault diagnosis method based on EBWO-SVM is proposed. Firstly, the BWO algorithm is improved by introducing quasi-opposition-based learning strategy and cyclone foraging strategy, and then the EBWO algorithm and particle swarm optimization algorithm, grey wolf optimization algorithm, whale optimization algorithm, and beluga optimization algorithm are tested for optimality seeking on six test functions, which verifies the superiority of the EBWO algorithm. Secondly, the EBWO algorithm is used to optimise the kernel function parameters g and C in SVM so as to improve the classification ability of SVM. Finally other methods are proposed to compare with the EBWO-SVM model. The results indicate that the constructed EBWO-SVM transformer fault diagnosis model improves the comprehensive diagnostic accuracy by 7.7%, 9.7%, 11.6%, and 15.4% compared with BWO-SVM, WOA-SVM, GWO-SVM, and PSO-SVM, respectively, and is more stable, which verifies the feasibility and effectiveness of the EBWO-SVM model.