Electronic medical records are the archives to note patients’ health conditions during treatment, where a large number of medical entities are scattered throughout the text and a wealth of medical information is contained. Existing relation extraction models in the medical field mainly utilize the relation classification method to recognize the semantic relation between two medical entities. Chinese electronic medical records have the characteristic of a dense distribution of medical entities in the text. In response, this study proposes a method based on condition hint and sequence labeling to extract relation triples. In this approach, the relation triple recognition task is converted to a sequence labeling task. The head entity and relation type in a relation triple combine to form condition hint information, and the model recognizes tail entities relevant to the condition hint information from the text of electronic medical records by sequence labeling. The experimental results on an electronic medical record dataset show that this method can be applied to recognize relation triples in Chinese electronic medical records.