In this paper, we introduce a novel technology utilizing relation networks with metalearning for intelligent machinery fault diagnosis, particularly in scenarios with limited labeled samples, employing the principles of few-shot learning (FSL). The proposed approach was characterized by its flexibility, simplicity, and a versatile framework. FSL facilitated the recognition and classification of new classes, requiring only a small number of samples from each category. The core of this method was the adaptive end-to-end training of the relation network (RN) from scratch. During the metalearning stage, the RN learned a deep distance metric, enabling the comparison of limited fault samples within episodes, and these episodes were simulated within the context of few-shot settings. Following the training process, the RN demonstrated the capability to classify samples from new classes by computing relation scores. Notably, it could also compare query samples with the limited samples from each new class without the need for further network updates. Experimental verification solidified the effectiveness of the proposed RN method, showcasing its robust classification abilities and achieving a relatively high level of accuracy. This technology holds promise for enhancing fault diagnosis in intelligent machinery, particularly in scenarios where labeled samples are scarce.