“…Bayesian network is modeled in form of the directed acyclic graph, the nodes represent variables in the system, the edges with the direction represents represent the causal relationship among variables, conditional probability represent the relevancy among variables, it also could be expressed and analyze the multi-source information, which helped it deal with uncertainty problems [25,26] . Both Bayesian network inference and approximate reasoning exist NP problem [27,28] , it could be reasoning efficiently in certain conditions by combining tree algorithm, Gibbs sampling algorithm, importance sampling algorithm and so on [29,30] . Bayesian network models through the following three ways: Artificial modeling method based on expert [31] , Machine learning modeling method based on sample data [32,33] and reasoning modeling method based on knowledge [34] .…”