To overcome the shortcoming of subjectivity and unicity in those fusion methods

the uncertainty measure and supporting degree of prior distribution was introduced; and the prior information weighting fusion was proposed on the basis of the uncertainty measure combined with the supporting degree. First

the uncertainty of testability prior information was described using β distribution and the prior distribution hyper-parameter was determined by different prior information. Then

the uncertainty measure and supporting degree was proposed as the weight coefficient of prior information

and the corresponding fusion algorithm was designed. Finally

using the mixed prior distribution and combining with the testability field test data

the posterior results of Bayes fusion evaluation were caculated. This method considered both the proximity of the measure parameter to the real situation

and also the level of supporting degree between different prior information. The example shows that the lower limit of confidence estimation based on the proposed method increases 0.7% than the traditional method at the same confidence.