The purpose of relational extraction is to extract the relational triples in a given text. Recent work shows that Large Language Models(LLMs) achieve excellent performance in information extraction, especially in the few-shot learning. An important challenge in the medical field is the long-tail problem, which has not received much attention in the context of LLMs. Therefore, we propose a novel approach, the Knowledge Enhancement-Prompted Relationship Extraction (KE-PromptRE), to improve the performance of LLM for long-tail problems of relationship extraction in the medical domain. KE-PromptRE transforms labeled data in the medical domain into generative data accepted by LLM and infuses it into LLM to enhance LLM’s performance in the medical domain. In addition, we use Curriculum Learning (CL) to infuse knowledge data into LLM in batches according to difficulty to improve the performance of LLM on the relation extraction. The results showed that our approach performed 9.5% and 2.3% above baseline on the Critical Illness entities and relationships Corpus (CIC) and Chinese Medical Information Extraction (CMeIE) datasets.