Abstract: At present, commonly used open-pit mine slope stability analysis methods, such as the limit equilibrium method and the finite-element strength reduction method, rely on the safety factor as the sole criterion, which presents certain limitations. This study proposed the use of a fuzzy measure method, which effectively handled fuzziness and uncertainty, to conduct slope stability analysis for open-pit mines. To address the difficulty of accurately determining fuzzy parameters in this method, a Genetic Algorithm (GA)-optimized BP neural network (GABP) model was designed to predict the fuzzy parameters. The experimental results showed that the mean relative errors of the GABP-predicted fuzzy parameters \xi and η were 3.66% and 3.25%, respectively, both lower than those of the BP neural network. By substituting the fuzzy parameters predicted by the GABP model into the fuzzy measure method, the stability of the slope in the Gaocun Iron Mine was analyzed. The calculated slope failure probability was 0.155 4, indicating overall stability with local instability, which was highly consistent with field monitoring results. The finite-element strength reduction method and the Bishop method were further used to validate the accuracy of the GABP-optimized fuzzy measure method. The results showed that the three methods yielded consistent outcomes, whereas the GABP-optimized fuzzy measure method required lower computational cost and provided higher efficiency. It also accurately characterized the progressive evolution of the slope from stability to failure, which better matched practical engineering conditions.