Learning from the past can be invaluable in enhancing risk resilience and developing prevention strategies. One common approach to investigating operational risks is analyzing safety records, which contain various incident data. However, traditional operational risk analysis methods have several limitations. The first significant drawback is that safety records are often documented as unstructured or semistructured data, and the database can be enormous, making it challenging to extract risk information efficiently. Further, the traditional risk assessment method per the ISO 31000 standard is qualitative and subjective, which can lead to inconsistent and inaccurate risk computation, especially when dealing with hazards that have multidimensional consequences. To address these issues, a new method, called the risk-based knowledge graph (RKG), is developed in this paper. The RKG method integrates text mining and analytic hierarchy process (AHP) risk assessment with knowledge graphs for operational risk analysis. This approach provides a systematic method for industrial practitioners to examine operational risk by using AHP risk assessment and graphical semantic networks to illustrate cause-and-effect relationships between risk entities. The use of text mining improves the efficiency of risk information extraction, while the use of AHP risk assessment enhances the consistency and accuracy of risk computation. To evaluate the accuracy and efficacy of RKG, a case study of a computer numerical control manufacturer is conducted. Overall, the RKG method shows promise in addressing the limitations of traditional operational risk analysis methods. It provides a more efficient and accurate way to extract and analyze risk information, making it easier for industrial practitioners to evaluate and manage risks associated with complex hazards.