Aiming at the problems of damage caused by short-circuit faults and low fault identification rate of transmission lines, a fault identification method combining VMD-PE and siamese neural networks ( SNN ) is proposed. For determining the number of decomposition layers K, use the instantaneous frequency mean to optimize VMD parameters, decompose the three-phase voltage at fault by VMD, calculate the permutation entropy of each component after decomposition, and use them as the fault features; input the fault features into the trained SNN for similarity measurement, compare the similarity between the two input samples to determine the type of short-circuit fault on the transmission line. The feasibility of the method is verified by simulation experiments, and compared with other classification methods, the accuracy and superiority of the method are proved.