A new method for transformer fault diagnosis has been proposed to address the issue of low diagnostic accuracy. This approach involves the use of a multi-strategy improved slime mould algorithm (ISMA) for phase optimization in conjunction with a hybrid kernel support vector machine (HSVM). Firstly, principal component analysis (PCA) is employed to eliminate information redundancy among variables and reduce the dimensionality of the dataset. Secondly, the slime mould algorithm (SMA) is introduced, and a Logistic chaotic mapping, quadratic interpolation, and adaptive weight multi-strategy improved SMA are proposed to enhance the convergence speed and local search capability of the SMA algorithm. Subsequently, optimization tests are conducted by comparing the improved SMA algorithm with the original SMA, WHO, and GWO algorithms to validate its superiority. Finally, the improved SMA algorithm is utilized in a phased manner for parameter optimization of HSVM, leading to the construction of the ISMA-HSVM transformer fault diagnosis model. After inputting the dimension-reduced feature data into the HSVM model and comparing it with BPPN, ELM, and SVM, the diagnostic accuracy of the HSVM model improved by 5.55%, 8.89%, and 5.55%, respectively. By optimizing the HSVM model using ISMA and comparing it with WHO, GWO, and SMA algorithm optimizations, the accuracy increased by 13.33%, 12.22%, and 5.55%, respectively. Specifically, the diagnostic accuracy of the ISMA-HSVM model reached 93.33%. The experimental results indicate that the proposed model effectively enhances fault diagnosis classification performance and demonstrates a high level of diagnostic accuracy.