“Joint entity recognition and relation extraction are important tasks in natural language process-ing. While some previous work has recognized the importance of relation information in jointextraction, excessively focusing on relation information without utilizing entity information maylead to information loss and affect the identification of relation tuples. Additionally, ignoring theutilization of original information may result in the loss of hierarchical and semantic information,further reducing the richness of information.To address these issues, we propose a bidirectionalinformation updating mechanism that integrates entity and relation information, iteratively fus-ing fine-grained information about entities and relations. We introduce a long-term memory gatemechanism to update and utilize original information using feature information, thereby enhanc-ing the model’s ability for entity recognition and relation extraction. We evaluated our approachon two Chinese datasets and achieved state-of-the-art results.”