Abstract: To address the high safety risks and frequent accidents associated with oil tanker loading and unloading operations, this paper proposes a data-driven risk assessment method based on Bayesian networks. Guided by systems engineering theory, a three-layer Bayesian network evaluation model comprising 34 nodes is constructed. Using the inference principle of the expectation-maximization algorithm, the conditional probabilities of the network nodes are computed to quantify risk levels within the model. The rationality and reliability of the model are verified through sensitivity and effectiveness analyses. Validation using data from 20 actual tankers demonstrates that the model’ s output aligns with risk levels assessed by port security personnel and can accurately evaluate the risks during oil tanker loading and unloading operations. The proposed model and method are applicable for assessing safety risk levels in oil tanker operations and can serve as a reference for safety evaluations of loading and unloading operations for other types of dangerous goods carriers.