Aiming at the problem that the existing testability indicator evaluation method fails to consider the characteristics of the testability dynamic growth at different levels in the equipment development stage， which leads to the low confidence of the testability evaluation， under the testability growth conditions， an indicator dynamic evaluation method based on hierarchical Bayes network model is proposed. A hierarchical Bayes network model for testability indicator evaluation could be established by equipment structural characteristics with the testability indicators as transmission parameters. When considering the testability growth test strategy for delaying correction， the testability sequential growth constraints are given. Meanwhile， the testability data of each stage at different levels of nodes are used to check the testability growth trend by Fisher's exact test method， and the number of growth stages can be determined. Then a priori parameter estimation method using the maximum entropy model and the improved Gompertz model is proposed， so that the Bayes theorem and the prior information of the nodes at each level in the development stage could be used to determine the prior distribution. Finally， the posterior distribution of the top-level node testability indicator is determined based on the hierarchical Bayes network fusion reasoning algorithm in order to realize dynamic evaluation， and an actual case is given to prove the effectiveness of the method. The results show that the method has more accurate and reasonable indicator evaluation conclusion than direct application of Beta distribution.