With the successful development of deep learning techniques in recent years, deep neural networks have achieved excellent results in both computer vision and natural language processing by relying on large-scale datasets but still face significant challenges in solving the problem of learning from few-shot. Inspired by the ability of humans to learn to recognize objects as a way to simulate the cognitive process of learning from a small sample size, few-shot learning is a hot topic of research in deep neural networks today. It is also a significant and challenging problem. This paper first introduces the research background and definition of few-shot learning, introduces the relevant models, and summarizes and analyzes the common approaches to the problem of few-shot learning based on deep neural networks at the present stage, which are divided into four types: data augmentation, model fine-tuning, metric learning and meta-learning. Finally, popular datasets for few-shot learning are described, the paper is concluded and future research directions are discussed.