To　address　the　issue　of　previous　methods　for　estimating　rock　shear　strength　parameters　lacking　the　ability　to　reflect　and　quantify　uncertainties,　a　rock　shear　strength　parameter　uncertainty　estimation　method　based　on　Gaussian　process　regression　(GPR)　is　proposed　for　conducting　probabilistic　uncertainty　analysis.　Utilizing　the　rock　strength　parameter　dataset,　Gaussian　process　theory　is　employed　to　establish　the　mapping　relationship　between　rock　uniaxial　compressive　strength　(UCS)　and　tensile　strength　(UTS)　with　shear　strength　parameters　using　various　kernel　functions.　Through　maximizing　the　logarithmic　marginal　likelihood　function,　the　hyperparameter　of　the　GPR　model　is　optimized,　and　then　the　appropriate　kernel　function　and　GPR　model　are　determined　according　to　the　prediction　effect　and　uncertainty　degree.　The　results　indicate　that　under　given　UCS　and　UTS　data,　it　is　advisable　to　utilize　the　Matern　kernel　function　for　developing　the　cohesion　GPR　model　and　the　rational　quadratic　kernel　function　for　constructing　the　internal　friction　angle　GPR　model.　Compared　with　conventional　machine　learning　methods,　the　GPR　method　not　only　provides　accurate　predictions　of　rock　shear　strength　parameters　but　also　offers　insights　into　the　degree　of　prediction　uncertainty,　demonstrating　strong　scientific　validity　and　interpretability,　thereby　validating　the　feasibility　and　efficacy　of　the　GPR　model.