The high performance of artificial intelligence （AI） is usually dependent on large and sufficient data to train parameters. How to improve the predictive performance in the case of insufficient data， i.e.， few-shot learning， is one of the important research subjects in the AI field. An image interpolation-based few-shot learning strategy is proposed， whose feasibility is verified in the task of handwritten digit image recognition. The few-shot learning performance of dense neural network and convolutional neural network in MNIST and USPS handwritten digit image recognition is systematically studied. The calculation results show that the image interpolation-based data enhancement method can evidently promote the characteristics extraction ability and learning efficiency of neural network in small sample data. Moreover， selecting the appropriate scaling coefficient of image interpolation can further optimize the few-shot learning performance of neural network.