Non-intrusive load monitoring (NILM) is an important part of intelligent power utilization and energy saving techniques and has attracted extensive attention. Due to the superior performance of newly-developed deep learning methods in various tasks in recent years, some representative deep learning methods have been successfully applied to the load decomposition task in NILM. To systematically summarize the research status and progress of deep learning methods applied to NILM, this study focuses on analyzing and summarizing the research literature on deep learning based NILM in recent years. Firstly, the NILM framework is outlined, and then the feature extraction method and the public data set of NILM are introduced. In addition, the load decomposition methods based on deep learning in NILM are analyzed and summarized. Finally, the study points out several challenges in this field and provides an outlook on its opportunities and future research directions.