Abstract: This paper aims to address the issues of insufficient temporal knowledge graphs for open-pit coal mine slope safety, difficulties in effectively sharing data resources, and limitations in handling dynamic knowledge. A method for constructing the temporal knowledge graph for open-pit coal mine slope safety is proposed. Based on the "head entity-relationship-tail entity-timestamp" descriptive model, the open-pit coal mine slope safety temporal knowledge graph was defined as the formal expression of the concepts and relationships of four types of ontologies: slope instability accidents, emergency response plans, slope monitoring data, and slope risk identification methods, arranged in a chronological sequence. First, based on expert prior knowledge, literature materials, and other sources, a model layer was established, and the abstract definitions of the four ontology concepts—slope instability accidents, emergency response plans, slope monitoring data, and slope risk identification methods—were analyzed. The semantic relationships between these concepts were examined to construct the framework of the knowledge graph. Then, based on this knowledge graph framework, knowledge extraction was carried out from vast amounts of data such as professional literature and monitoring data. Redundant entities were integrated, entity matching was performed, and knowledge was stored in a graph database, ultimately completing the construction of the data layer for the entity nodes, node attributes, and node relationships in open-pit coal mine slope safety. On-site verification results showed that the proposed method for constructing the temporal knowledge graph of open-pit coal mine slope safety is feasible. It can fully represent the concepts of open-pit coal mine entities and their relationships, and when searching for slope safety accidents, related slope monitoring data, risk identification methods, and emergency response plans can be discovered, providing technical support for open-pit coal mines facing slope accidents.