Abstract: Bayesian network is a graph model which combines probability statistics and graph theory.It has been successfully applied in many fields.However,it is very difficult to build Bayesian network only depending on the domain knowledge of experts.Therefore,learning Bayesian network structure from data has become a key issue in this field.Bayesian network structure learning is a NP-hard problem because the search space is too large.According to the chara-cteristics of physarum polycephalum in the process of foraging to retain an important feeding pipeline,the original search space was reduced by combining the relevant mathematical model of physarum polycephalum and the theory of conditional mutual information.In this paper,the obtained undirected graph is used as the basic framework of the network,and then the mountain climbing method is used to determine the direction of the skeleton and get the correspon-ding topological ordering.Finally,the order of nodes is used as the input of K2 algorithm to get the final network.The network topology structure and score are selected as the evaluation index,and comparative experiments are carried out on multiple data sets.Experiments show that the proposed algorithm has higher accuracy in network reconfiguration and raw data matching.

Key words: Bayesian network, Independence condition mutual information, Physarum polycephalum, Structure learning