Abstract: Joint entity and relation extraction is a foundational task for knowledge graph construction, which aims at extracting relational triples from unstructured text. To address the issues of the redundant annotations in corresponding matrix and insufficient interactions between subjects and objects, a joint model based on biaffine entity pairing and cascade tagging scheme is proposed. First, the potential relations in the sentence are predicted by performing a multi-label classification task, which eliminates the redundant relations in the relation-specific entity identification module. Then, the integrated candidate entity representations are passed through a biaffine network to enhance the interactions between subjects and objects, resulting in an entity pairing matrix that contains only candidate entities and reduces redundant annotations in the entity pairing phase. Next, a cascade tagging scheme is applied to identify relation-specific entities, and the identified entities are combined with the entity pairing matrix to form relational triples. Finally, the effectiveness of the proposed model is validated by comparative experiments and ablation study conducted on four public datasets. The experimental results demonstrate the proposed model achieves significant performance improvements over current baseline methods in terms of standard evaluation metrics, while effectively mitigating both the redundant annotation problem and the issue of insufficient entity interactions.