Abstract: In order to overcome the defects existing in the lower efficiency of structure learning caused by directly applying particle swarm algorithm to it, i.e. the search space is too large; a hybrid particle swarm algorithm for Bayesian network structure learning is presented based on unconstrained optimization problem. Firstly, for the algorithm, an unconstrained optimization problem is established and solved; the edges in the undirected graph corresponding to the optimal solution could provide a search range for structure learning and reduce the search space of particle swarm algorithm; then, Bayesian network structure learning is completed in the reduced space. Therefore, the leaning efficiency of particle swarm algorithm is raised. The simulation results indicate that the proposed method is able to quickly and accurately learn the optimum Bayesian network structure.