Aiming at the problems of traditional neural network in mining transformer time series relationship, poor generalization of classification and low accuracy of classification for heterogeneous data, this paper proposes an improved transformer fault diagnosis model based on bidirectional recurrent neural network. The model extracts features through bidirectional recurrent neural network, fuses the features at the front and back time, classifies the feature data using multi?kernel learning support vector machine method, and fuses the features in multi?kernel learning support vector machine, so as to improve the accuracy of feature data classification. The accuracy and validity of the bidirectional recurrent neural network based on multi?kernel learning support vector machine model are tested through numerical simulation analysis of the temporal channel length for sequential network diagnostic performance, the influence of and multi?kernel learning on the generalization ability of support vector machines, and the influence on heterogeneous data processing capabilities. The experimental results show that the diagnosis performance of the bidirectional recurrent neural network based on multi?kernel learning support vector machine is better, and the prediction accuracy of the model is higher than that of several commonly used neural networks.