Aiming at the problems of low efficiency and low accuracy of fault diagnosis of electromagnetic current transformers, a fault diagnosis method based on variational mode decomposition (VMD) and sample entropy is proposed. The original fault signal is decomposed into an Intrinsic Mode Function (IMF) series through VMD and optimized. The sample entropy is calculated as the feature value of the new transformer feature extraction object, which is combined with the common timefrequency domain feature index to input the Knearest neighbor classifier for training. Matlab simulation experiments show that the new characteristic index of this method is effective and feasible for fault diagnosis of lowvoltage current transformers, which can provide a reference for fault diagnosis of the electromagnetic current transformer.