Aiming at the problem of low accuracy of transformer fault diagnosis, this paper proposes a power transformer fault diagnosis model based on improved parasitic predation algorithm ( IPPA) and optimized probabilistic neural network (PNN). Firstly, principal component analysis (PCA) is used to reduce the dimensionality of fault data to reduce invalid features, then use multiple strategies such as chaotic reverse learning, Cauchy mutation operator and the weight of linear decreasing function fused with beta distribution to improve the hunt-prey algorithm (IPPA) and its optimization ability, and use the improved IPPA algorithm to optimize the smoothing factor of the PNN network to improve the classification accuracy and robustness of the PNN. Finally, the PCA-processed data is input into the IPPAPNN model for fault diagnosis and testing based on the transformer data. The test results show that the accuracy of the IPPA-PNN model reaches 93%, which is 7% and 10% higher than that of the PPA-PNN and PSO-PNN models, and can effectively improve the fault diagnosis performance of the transformer.