In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis method based on improved SSA optimized mds-svm is proposed. Firstly, multi dimensional scaling (MDS) is used to extract features from 20 dimen-sional transformer fault feature data to reduce the sparsity and multicollinearity of high-dimensional data; Then, the paper in-troduces the sap swarm algorithm (SSA) and improves the algorithm by adding trust mechanism and mutation to improve the convergence speed and ability of the algorithm. By comparing with the original SSA, PSO, GWO and β-GWO algo-rithm is tested and compared to verify the superiority of the improved SSA algorithm. Finally, the improved SSA algorithm is used to reduce the dimension of MDS and optimize the parameters of support vector machine (SVM) to build a new fault diagnosis model. The fault diagnosis accuracy is analyzed and compared with the SVM fault diagnosis model optimized by common algorithms, BP neural network (BPNN), k-nearest neighbor (KNN) and random forest (RF) fault diagnosis models. The results show that the accuracy of MDS-SVM transformer fault diagnosis model based on improved SSA is higher than that of other algorithm models, and the generalization ability is stronger.