Amid the vigorous development of the knowledge graph, relation extraction, as a key part of information extraction, has attracted increasing attention from researchers. In general, relation extraction can be divided into template-based extraction and machine learning-based extraction. Later, with the extensive application of the extraction methods based on deep learning, the performance of relation extraction has been greatly improved. In this study, the time sequence method is employed to summarize the extraction methods of binary relations in a restricted domain. This study first briefly introduces the concept, data set, and evaluation indicators of relation extraction. Then it systematically sorts out the related extraction methods and highlights the current research on the relation extraction methods based on deep learning. Finally, it analyzes the future research direction and application of relation extraction.