Based on background knowledge represented as a Bayesian network, this paper presents a BN-EJTR method that computes the interestingness of frequent items and frequent attributes, and prunes. BN-EJTR seeks to find inconsistent knowledge relative to background knowledge and to resolve the problems of un-interestingness and redundancy faced by frequent pattern mining. To deal with the demand of batch reasoning in Bayesian networks during computing interestingness, BN-EJTR provides a reasoning algorithm based on extended junction tree elimination for computing the support of a large number of items in a Bayesian network. In addition, BN-EJTR is equipped with a pruning mechanism based on a threshold for topological interestingness. Experimental results demonstrate that BN-EJTR has a good time performance compared with the same classified methods, and BN-EJTR also has effective pruning results. The analysis indicates that both the pruned frequent attributes and the pruned frequent items are un-interesting in respect to background knowledge.