To predict the bridge structure performance based on inspection information and the priori model of bearing capacity, the dynamic measure of structural performance over time is treated as a time series, a Bayesian dynamic linear model (DLM) is then introduced. Considering the time-dependent characteristics of structural performance of the considered bridge, this paper proposes the probability method of bridge resistance degradation predication. State equation and observation equation of resistance degradation are established with Bayesian dynamic linear model. Combining parameters’ prior information with the early resistance observation data containing noise, the resistance degradation state parameters are deduced with Bayesian Posterior Probability. A dynamic linear model is built to forecast the short-term trend of structural resistance. The one-step-ahead forecast distribution and the filtering distribution are determined for Bayesian dynamic updating. To allow for the epistemic uncertainty in variance estimation based on inspection information, a discount factor approach is made for specification of unknown variance matrix. Finally, a RC girder is taken as an illustration example to demonstrate the applicability of the proposed method.