Aiming at the problem of insufficient samples of infrared aircrafts and low accuracy of fine-grained classification， a method of infrared aircraft few-shot classification based on meta learning is proposed. Based on meta learning and combined with multi-scale feature fusion， this method can effectively extract commonness among different classification tasks while reducing computation， and then classify different tasks with fine-tuning. The experiments proved that this method could improve the classification accuracy of mini-ImageNet dataset while reducing the calculation amount by about 70%. The accuracy of fine-grained classification for infrared aircrafts with few samples reached 92.74%.