The prolonged operation of the large aperture radio telescope will lead to slight distortion of elevation axis, increasing the risk of fatigue of the rolling bearing which is the core component of the elevation axis. This can lead to a decline in the bearing life and the pointing accuracy of the telescope, which will greatly affect the telescope's high performance service. Investigating the fault identification method for the elevation bearing can provide an important support for the high-performance operation of the telescope antenna. In this paper, a few-shot meta-learning fault identification (FMFI) method based on meta-learning is proposed in order to achieve accurate fault identification of elevation bearing under limited data and complex working conditions. The raw signals of different working conditions are first converted to time-frequency images data and then randomly sampled for different learning tasks according to the meta-learning protocol. Under limited sample data condition, the FMFI method can obtain universal prior knowledge from the sample in the training task to achieve accurate and fast fault identification in unknown testing tasks. The variable load bearing data set which is similar to the working condition of telescope elevation bearing is selected for experiment, and the experimental results show that the FMFI method is high accurate and reliable, providing strong technical support for the operation, maintenance, and high-quality service of large aperture radio telescope.