In order to solve the problems of low speed of large slewing bearings, high background noise, and difficulty in applying conventional acoustic emission diagnostic methods, an acoustic emission signal processing method based on the combination of gray image and ResNet model was proposed. The acoustic emission signal was encoded into a two-dimensional grayscale image, and the grayscale image obtained by the acoustic emission signal encoding was identified through the ResNet model, and the fault diagnosis of the large slewing bearing was realized through the training model.A certain type of large-scale slewing bearing was tested. The results show that the fault diagnosis accuracy of the slewing bearing can be significantly improved by using the two-dimensional gray-scale diagram of the time series as the basis;compared with the traditional method, the proposed method has better generaliztion performance and robustness, and it can be well applied to the fault diagnosis of large slewing bearings in actual working conditions.