In recent years, artificial intelligence supported by big data has gradually become more dependent on deep reinforcement learning. However, the application of deep reinforcement learning in artificial intelligence is limited by prior knowledge and model selection, which further affects the efficiency and accuracy of prediction, and also fails to realize the learning ability of autonomous learning and prediction. Metalearning came into being because of this. Through learning the information metaknowledge, the ability to autonomously judge and select the appropriate model can be formed, and the parameters can be adjusted independently to achieve further optimization. It is a novel method to solve big data problems in the current neural network model, and it adapts to the development trend of artificial intelligence. This article first briefly introduces the research process and basic theory of metalearning and discusses the differences between metalearning and machine learning and the research direction of metalearning in big data. Then, four typical applications of metalearning in the field of artificial intelligence are summarized: few-shot learning, robot learning, unsupervised learning, and intelligent medicine. Then, the challenges and solutions of metalearning are analyzed. Finally, a systematic summary of the full text is made, and the future development prospect of this field is assessed.