Aiming at the problem that the unbalance of transformer fault data weakens the ability of fault classification, a transformer fault diagnosis method based on hybrid sampling and improved honey badger algorithm ( IHBA) and optimized support vector machine (SVM) is proposed. Firstly, K-nearest neighbor denoising, K-means and SMOTE are used for hybrid sampling of data to alleviate the shift of diagnosis results to the majority class. Then, the traditional honey badger algorithm (HBA) is improved by using tent mapping, roulette random search mechanism and optimal individual perturbation strategy, and the SVM parameters are optimized by IHBA to further improve the transformer fault identification ability. Finally, the simulation results of the proposed method show that, compared with the traditional transformer fault identification method, the fault diagnosis model combining K-Nearest Neighbor denoising, K-means, SMOTE hybrid sampling and IHBA-SVM obtains the highest macro F1 and micro F1 values, reaching 0. 877 and 0. 886 respectively, which indicates that the proposed model not only has higher overall classification ability, but also can better identify minority faults.