Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the belief of rule. Now,most of uncertain reasoning models represent the belief of rule by conditional probability. However,it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper,AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability,then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the existed methods that the forms of focal sets influence updating result. Some examples are given to illustrate the effectiveness of the proposed method. Updating or conditioning a body of evidence modeled within the DS framework plays an important role in most of Artificial Intelligence (AI) applications. Rule is one of the most important methods to represent knowledge in AI. The appearance of uncertain reasoning urges us to measure the Now, most of uncertain reasoning models represent the belief of rule by conditional probability. However, it has many limitations when standard conditional probability is used to measure the belief of expert system rule. In this paper, AI rule is modelled by conditional event and the belief of rule is measured by conditional event probability, then we use random conditional event to construct a new evidence updating method. It can overcome the drawback of the concluded methods that the forms of focal sets influence updating result. given to illustrate the effectiveness of the proposed method.