Abstract: In this paper, we presented a digital characterization method of abrasive particles based on deep learning and Mask R-CNN convolutional neural network that enabled us to solve the problem in equipment wear fault diagnosis such as high difficulty of abrasive particle identification and great influence of subjective experience. This method was used to transfer learning of training the wear particle recognition model based on the Mask R-CNN network to identify and segment the wear particles in the image, and then using the Suzuki85 algorithm, iterative algorithm, and proportional calculation to calculate the true size of the wear particles. It solved the problem of difficult quantitative analysis in abrasive particle analysis. The experimental results showed that the wear particle recognition model based on the Mask R-CNN network (using the R-101-FPN backbone network) can identify multiple abnormal wear particles in the image, and the comprehensive accuracy rate and recall rate came up to mainstream standard level of image recognition. Supplemented by the above algorithm, it successfully implemented quantitative evaluation and analysis of wear images, and was practical and valuable for promoting the automatic development and industrial application of equipment wear fault diagnosis.