In order to overcome the difficulty for the parameters identification of occupant restraint system caused by the measured uncertainty, this paper proposed an uncertain identification method for the parameters of occupant restraint system based on Bayesian inference, which combined Markov Chain Monte Carlo (MCMC) sample and surrogate model. This method firstly obtains the prior distributions of identified parameters and measured responses, and then the MCMC sampling is applied to the joint probability density of unknown parameter. Then, the marginal posterior probability distributions of scale factor of webbing and rate of flow can be calculated. Compared with the traditional method of determined identification, the identified results show that the Bayes inference method for uncertain parameter identification not only obtains the probability distributions effectively, but also ensures the global convergence of identified parameter.