To solve the problem of low accuracy of tradition transformer fault diagnosis methods, a transformer fault diagnosis method based on the improved sparrow search algorithm was proposed. First, the oppositionbased learning (OBL) is introduced to optimize the selection of the population to improve the global optimization ability of the sparrow search algorithm.Then use the ISSA to dynamically optimize the kernel function parameters and penalty coefficients of the support vector machine, and obtain the fault diagnosis model of the support vector machine optimized by the ISSA based on DGA. The original data is processed through very sparse random projection to remove redundant features. At last input the processed data into ISSASVM for fault diagnosis, and compare it with GWOSVM, PSOSVM and SSASVM. The results show that the fault diagnosis rate of the ISSASVM is 92%, which is 1067%, 8% and 533% higher than that of GWOSVM, PSOSVM and SSASVM. So it can predict the operating status of the transformer more accurately.