Dynamic Bayesian networks can be well dealt with the time-varying multivariable problem. The state model based on Dynamic Bayesian networks can more accurately describe the relationship between the system state and the influencing factors. In this paper, the width of the reasoning is used to simplify the amount of data in the reasoning process. Multi-step state prediction is achieved by extending time-slice. Experiment has shown that the proposed algorithm can achieve better prediction results.