<p>Hyper-relational knowledge graphs can enhance the intelligence, efficiency, and reliability of industrial production, facilitate equipment coordination, and optimize supply chains. However, due to current data and technology limitations, the construction of knowledge graphs in the industrial domain remains imperfect. Link prediction can effectively address this issue. Therefore, this paper constructs a hyper-relational knowledge graph for mine hoists to perform link prediction tasks. In traditional triple data sets, link prediction involves masking entities or relations to predict MRR and Hits@K. Although significant progress has been made in link prediction on traditional triple data sets, research on hyper-relational data sets is still lacking. This paper proposes a new method for solving link prediction problems in hyper-relational knowledge graphs—HyperKGLinker, which specifically addresses link prediction in hyper-relational knowledge graphs. This method innovatively integrates hyper-relational text data and hyper-relational graph data. Compared to baseline models, our model shows improved predictive performance across different data sets, indicating a significant enhancement in the accuracy and efficiency of link prediction in hyper-relational knowledge graphs. Future Research Work: (1) Integrate generative large language models like GPT to further optimize and expand the self-constructed hyper-relational knowledge graph of mine hoists, thereby exploring the generalization ability of HyperKGLinker. (2) Investigate methods for model interpretability to enable users to understand the model’s prediction results and decision processes, increasing its credibility and usability. </p>