Joint entity recognition and relation extraction are important tasks in natural language processing. While some previous work has recognized the importance of relation information in joint extraction, excessively focusing on relation information without utilizing entity information may lead to information loss and affect the identification of relation tuples. Additionally, ignoring the utilization 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 bidirectional information updating mechanism that integrates entity and relation information, iteratively fusing fine-grained information about entities and relations. We introduce a long-term memory gate mechanism to update and utilize original information using feature information, thereby enhancing the model’s ability for entity recognition and relation extraction. We evaluated our approach on two Chinese datasets and achieved state-of-the-art results.