Abstract: An improved scoring search algorithm based on information flow is proposed for Bayesian network structure learning. Firstly, the 0/1 optimization problem is constructed based on the information flow for global causal analysis, and the optimal initial network structure is obtained. Then, the search space is generated based on the initial structure, and the optimal structure arcs are searched by the greedy algorithm. At the same time, the arc direction is determined by the information flow, to achieve integrated learning of the Bayesian network structure. For the first time, the information flow is introduced into the structure learning of Bayesian network, optimizing the initial search space, realizing the synchronous determination of arcs and arc direction, and obtaining the approximate global optimal structure. Experiments show that the improved algorithm has higher accuracy and learning efficiency than other algorithms.