Bayesian networks can represent uncertain knowledge and perform inferential computational expressions， but due to the noise and size limitations of actual sample data and the complexity of network space search， Bayesian network structure learning will always have certain errors. To improve the accuracy of Bayesian network structure learning， a Bayesian network structure learning algorithm with the results of maximum frequent itemset and association rule analysis as the prior knowledge was proposed， namely BNSL-FIM （Bayesian Network Structure Learning algorithm based on Frequent Item Mining）. Firstly， the maximum frequent itemset was mined from data and the structure learning was performed on the itemset， then the association rule analysis results were used to correct it， thereby determining the prior knowledge based on frequent item mining and association rule analysis. Secondly， a Bayesian Dirichlet equivalent uniform （BDeu） scoring algorithm was proposed combining with prior knowledge for Bayesian network structure learning. Finally， experiments were carried out on 6 public standard datasets to compare the Hamming distance between the structure with/without prior and the original network structure. The results show that the proposed algorithm can effectively improve the structure learning accuracy of Bayesian network compared to the original BDue scoring algorithm.