Sleep staging is a vital process for analyzing polysomnographic recordings， which plays a key role in sleep monitoring and diagnosis of sleep disorders. Traditional manual sleep staging requires expertise， which is cumbersome and time-consuming. Deep learning constructs models by simulating the mechanism of human brain to interpret information， and has powerful automatic feature extraction and feature expression functions. Applying deep learning method to the research of sleep staging does not rely on manually designed features and can realize the automation of sleep staging. This article emphasizes on some typical automatic sleep staging studies since 2017， and conducts a systematic review of deep learning model applied in automatic sleep staging from two aspects of single-view and multi-view input. Then， the difficulties of deep learning model based on multi-view input are analyzed and its potential research value is pointed out. Finally， possible future research direction is discussed.