User-transformer relation is very important to the service deployment of business distribution integration, line loss analysis, etc. In order to obtain the accurate affiliation relation, a new user-transformer relation identification method based on quantum genetic algorithm (QGA) and kernel fuzzy C-means clustering (KFCM) is proposed in this paper. The main idea is: since the power meter of different areas has different voltage zero-crossing shift features, the affiliation identification is realized through comparing the zero-crossing shift near transformer with the classification result of zero-crossing shift on power meter which is obtained by KFCM. At first, in order to improving the accuracy and efficiency of clustering, the QGA is introduced to optimize the cluster center and kernel parameters of KFCM and a fitness function construction method based on inner-class distance and between-class distance is present. Besides, the niche coevolution strategy, the dynamic adjustment strategy and the Hadamard gate mutation strategy are proposed to enhance the optimization ability of QGA. Then, the simulation tests on benchmark function and UCI dataset character verify that the proposed method in this paper has better cluster accuracy and efficiency than standard KFCM. At last, the proposed method is applied to solve the user-transformer relation identification of practical low voltage area. Result shows that the power meter affiliation obtained by proposed method is the same as the real affiliation. The application effect is very good.