Considering the low accuracy of life stage identification caused by the large difference of sample distribution， the small number of life stage training samples and the unequal number of samples in different life stages， Model-Agnostic Meta-Transfer Learning （MAMTL） is proposed to identify the life stage of space rolling bearings. In MAMTL， model-agnostic meta-learning and transfer learning are combined to achieve multi-task synchronous parallel training instead of traditional iterative training. Multiple task loss functions in MAMTL use unlabeled samples under different working conditions and a small number of labeled samples under historical working conditions to jointly update the network parameters of MAMTL to seek global optimal solutions of them，which makes MAMTL have better generalization ability so that MAMTL has better domain adaptability than traditional transfer learnings when there are few labeled training samples under historical conditions. Moreover， a new prototype network is constructed in MAMTL to represent the samples of each class in historical working conditions as a prototype. Thus， the testing samples under current working conditions are classified by calculating the similarity between the testing samples and the prototypes， and the classification process does not need parameter learning， which can prevent the problem of large difference in identification accuracy of different classes under the unequal number of samples in different classes and the over fitting problem of network in the case of few labeled training samples， and then better improve the classification accuracy. The above advantages of MAMTL enable it to use few and unequal training samples known for the life stages under historical working conditions of space rolling bearings to perform high-precision life stage identification for the current testing samples. The life stage identification examples of a space rolling bearing demonstrate the effectiveness of the proposed MAMTL-based life stage identification method.