To handle the problems of difficulty in extracting fault features and low identification accuracy of traditional transformer fault identification methods, a novel identification method is proposed by fusing standard deviation-based multiscale fuzzy entropy ( SDMFE) and Harris hawks algorithm ( HHO) optimized support vector machine ( SVM). Firstly, multiscale analysis method based on fuzzy entropy is employed to quantify the complex dynamic characteristics of transformer vibration signals, and then extract fault features under multi-time scales. Subsequently, the fault features obtained by SDMFE are input into SVM for identifying transformer different faults. At the same time, to improve SVM recognition performance, an optimization strategy integrating HHO algorithm is introduced to select SVM parameters adaptively and accurately. Finally, a comparative experiment is carried out using the measured vibration signal of the transformer. Compared with different information entropies, different optimization strategies and different classifiers, the proposed method achieves the highest identification accuracy of 98. 56% and identification stability. The results show that the proposed method can effectively extract fault sensitive features and accurately identify transformer fault status.