<p>Knowledge graph completion has attracted significant attention due to predicting missing links in knowledge graphs. Recent advancements in embedding-based methods have demonstrated excellent performance in 1-to-1 relation prediction. However, these methods encounter difficulties in modeling complex relations, including 1-to-N, N-to-1, and N-to-N, resulting in less effective of complex relation prediction. To address the limitations of semantic representation caused by relational complexity in embedding-based methods, we propose the joint entity and relation embedding network (JERENet) for multi-relational knowledge graph completion. Specifically, JERENet can effectively capture complex relations by leveraging multi-relational features among entities and neighborhood structure information. JERENet also introduces adversarial training through the construction of adversarial samples. Our proposed method can effectively capture complex relations by leveraging multi-relational features among entities and neighborhood structure information. Extensive experiments on public datasets WN18RR and FB15k-237 reveal that JERENet shows superior or at least comparable performance to the state-of-the-art baseline methods in link prediction.</p>