In order to improve the accuracy of gear rack wear recognition under various operating conditions,considering the characteristics of lack of actual effective fault data and missing data mark of large modulus gear rack,a method of largemodule pinion and rack abrasive particle identification based on transfer learning neural with a new joint loss（LCNNE）was proposed.The LCNNE model was used as a feature extractor to extract features and an external classifier SVM was used for classification.It is found that the recognition rate of the LCNNE model on the abrasive data set reaches about 99%,and the recognition rate of the feature input SVM extracted by the integrated model of the method is 2% and 3% higher than that of the joint feature extracted by VGG19 and GoogleNet.t-SNE technology was used to visualize the features extracted from the last hidden layer of DCNN,VGG19,GoogleNet and LCNNE models,it was proved that LCNNE model has stronger feature expression ability and better recognition effect.