In order to realize the identification of transformer core looseness faultResNet convolution neural network (CNN) based on transformer voiceprint is proposed as the identification of core looseness fault. The different performance effects of the same ResNet-CNN in the cross entropy loss function (SE-ResNet-Dense) and the additive angular margin loss function (SE-ResNet-ArcLoss) which maximizes the classification boundary in the angle space of feature expression are comparedrespectively. The noise signals of the transformer core at the rated preload20% looseness and 40% looseness are collected through the transformer no-load experiment. The collected voiceprint signal is generated into the time-frequency matrix through the discrete Fourier transformand the Mel filter is used to reduce the dimension of the time-frequency matrix to generate the Mel spectrogram with greatly reduced size. The collected noise signal is made into a data set and input into the two models for training. The prediction result of the final test set on the model SE-ResNet-Dense is 90.753%and that on the model SE-ResNet-ArcLoss is 97.541%. The results show that SE-ResNet-ArcLoss are most suitable for transformer core looseness identification.