Object intrusion is among the primary causes of railway accidents. Typically， traditional deep-learning methods require numerous samples for network training； however， intrusion samples in railway settings are scarce and difficult to obtain. Thus， in this paper， a railway few-shot intruding-object detection method based on an improved metric meta-learning network is proposed. To better exploit the features of intruding objects during classification， a feature-extraction network based on the channel attention mechanism is proposed. A network based on fine-tuning of the class center is proposed for class-center correction to solve the problem of individual samples deviating in the feature space of insufficient samples. Additionally， a central correlation loss function based on the center loss and cross entropy is constructed for few-shot network training to improve the compactness of the same-class feature distribution in the feature space. In experiments on a public few-shot dataset called miniImageNet， the accuracy of the proposed method is 7.31% higher than the optimal accuracy of the classical few-shot learning model. In five-way five-shot ablation experiments using a railway dataset， the proposed channel attention mechanism and center-related loss function increase the mean average precision （mAP） by 0.86% and 1.91%， respectively. Additionally， the center fine-tuning and pretraining increase the mAP by 3.05% and 6.70%， respectively， and the total mAP improvement is 7.90%.