In response to the difficulty of feature extraction and pattern recognition in the identification of internal overvoltage types in distribution networks, this paper proposes a distribution network internal overvoltage identification method based on multi domain fusion feature extraction and Bayesian optimization Transformer BiGRU. Firstly, through multi domain fusion feature extraction, the 10 kV bus neutral point overvoltage signal is subjected to timefrequency, frequency domain, and time-frequency domain feature extraction to construct a ten dimensional feature vector with representational ability. Then, multiple sets of ten dimensional vectors of different types of overvoltage are input into the Bayesian optimization Transformer BiGRU network classifier to achieve recognition of five typical internal overvoltage types. To verify the effectiveness of the method, PSCAD simulation data and physical experimental platform fault waveforms were used to train and test the algorithm proposed in the paper using MATLAB, and the test results were compared with other methods. The results show that the recognition accuracy of the algorithm proposed in the article is as high as 99.11%, which has stronger feature extraction ability and higher recognition accuracy compared to other algorithms.