Bayesian networks are widely used in many fields. As a classifier, it is an effective classification method. Bayesian network classifier is one of the most challenging problems, which makes the Bayesian network classifier subject to many limitations in the application. Through the pairs of Bayesian network classifier algorithms' approximate treatment, it can effectively reduce the amount of calculation, and get satisfactory classification accuracy. This paper analyzes a way to change discriminative score function to generative score function by approximation method. This way is applied in Bayesian network classification algorithm. Finally, this paper uses the stability of new algorithm, proposes a new classifier through integration called Bagging-aCLL. It uses ensemble to improve the accuracy rate of the algorithm. The experiment test shows the classification accuracy rate of the algorithm have a good performance.