With the vigorous development of data acquisition technology， spatio-temporal data in various fields are accumulating continuously， so it is urgent to explore efficient spatio-temporal data prediction methods. Deep learning is a machine learning method based on artificial neural networks， which can effectively process large-scale complex data. Therefore， it is of great significance to study the spatio-temporal sequence prediction methods based on deep learning. In this context， the existing prediction methods are summarized. First， the application background and development history of deep learning in spatio-temporal sequence prediction are reviewed， and the related definitions， characteristics and classification of spatio-temporal sequence are introduced. Then according to the categories of spatio-temporal sequence data， this paper introduces the prediction methods based on grid data， on graph data， and on trajectory data. Finally， the above prediction methods are summarized， and some current problems and possible solutions are discussed.