Abstract: For the approximate equality constraint of Bayesian networks parameters, a normal distribution model is proposed. Then, Dirichlet distribution is utilized to approximate the normal distribution. And the super parameters of Dirichlet distribution are calculated by target optimization. Finally, Bayesian maximum a posterior (MAP) estimation is employed to estimate the parameters of Bayesian networks. With data sets of different sizes, the proposed method is compared with the other four main methods. The experiments results show that the parameter learning accuracy of the proposed method are better than the other four methods, especially in the case of small sample size. And the run time is greater than the other four methods. However, the multiplier of learning accuracy is higher than the multiplier of time increase under the condition of the same sample size.