Bayesian networks have been widely used in many scientific fields for describing the conditional independence relationships for a large set of random variables. This letter proposes a novel algorithm, the so-called p -learning algorithm, for learning moral graphs for high-dimensional Bayesian networks. The moral graph is a Markov network representation of the Bayesian network and also the key to construction of the Bayesian network for constraint-based algorithms. The consistency of the p -learning algorithm is justified under the small- n , large- p scenario. The numerical results indicate that the p -learning algorithm significantly outperforms the existing ones, such as the PC, grow-shrink, incremental association, semi-interleaved hiton, hill-climbing, and max-min hill-climbing. Under the sparsity assumption, the p -learning algorithm has a computational complexity of O(p 2 ) even in the worst case, while the existing algorithms have a computational complexity of O(p 3 ) in the worst case.