Due to the reasons as wear of materials and change of external environment during the operation of vital systems including engineering facilities and military equipment,the performance of the system is gradually decreasing or even fails,which will result in economic losses and loss of life. Therefore,in order to ensure the normal operation of the system,the Remaining Useful Life (RUL) prediction technology has attracted the attention of researchers. In the era of big data,the obtained monitoring data has the characteristics of high dimensions and strong coupling,so it is difficult to model the data by using the traditional RUL prediction method.The deep learning method can accurately establish the mapping relationship between the monitoring data and the degradation states or the life tag. This paper elaborates the research status of four typical deep learning models in the field of RUL prediction in detail,summarizes the advantages and disadvantages of these models,and discusses the development direction of deep learning based RUL prediction of complex degradation systems.