Bearing fault types are complex， and it is difficult to obtain enough training samples for each fault type under different working conditions. Convolutional neural network with training interference （TICNN）with wide convolutional kernel is introduced as the subnetwork of the Siamese network used to extract features， reducing the impact of industrial environment noise. Siamese network is a structure commonly used for few-shot learning. By inputting the same or different categories of samples for training， the mapping relationship between different attribute samples and features is learned， and the similarity between samples is used as measure index. The test sample is classified by finding the class of the nearest neighbor. Experimental results on the standard Case Western Reserve University （CWRU） bearing fault diagnosis benchmark dataset show that， in the case of limited data， the proposed model shows better results in fault diagnosis. The performance of the proposed few shot learning model exceeds the baseline model with a reasonable noise level when testing with the least training data in different noise environments， and the accuracy of fault diagnosis reaches 94.41%. When evaluating on test sets with new fault types or new working conditions， the proposed model also performs well.