Abstract: A large number of tunnel projects are being constructed or will be constructed in the mountainous areas of western China. However, they are several safety challenges in the construction of deep hard rock tunnels because of the complex topographic and geological conditions, strong geological tectonic activities, large burial depth, and high in situ stress level. Uncertainty of tunnel wall parameters is one of main factors that contribute to tunnel construction risk. The traditional deterministic back analysis method cannot reflect the uncertainty characteristics of tunnel wall parameters; therefore, within the framework of Bayesian theory, a probabilistic back analysis method based on integrating multi-source monitoring information was proposed for determining the surrounding rock parameters of deep hard rock tunnel. First, the uncertainty sources of three parameters——uniaxial compressive strength (UCS), crack initiation stress to UCS ratio, and tensile strength for the widely used damage initiation and spalling limit approach——were analyzed, and their probabilistic statistical characteristics were determined. Second, a multi-output support vector machine (MSVM) was optimized by particle swarm optimization (PSO) algorithm, and an intelligent response surface model was established to reflect the nonlinear mapping relationship between back-analyzed parameters and field monitoring data. Last, by combination with the Bayesian (B) analysis method, the B-PSO-MSVM model was established, and surrounding rock parameters were dynamically updated by applying the Markov Chain Monte Carlo simulation algorithm. The method was applied to a deep hard rock tunnel, and parameters from probabilistic back analysis were utilized to calculate the point change of the tunnel vault settlement and peripheral displacement convergence as well as the depth of excavation damage zones, and the results agreed well with the actual monitoring data. It is shown that this method can be used to back analyze multi parameters of surrounding rock quickly and probabilistically, and parameters updated can be applied for risk assessment in construction safety and structural reliability design for the hard rock tunnel.