贝叶斯网络是用来表示变量集合概率分布的图形模式，它提供了一种方便地表示概率信息的方法，它可以表示因果关系，但并不局限于因果关系。贝叶斯网对不确定性问题有很强的推理能力，近几年来受到众多研究者的重视。贝叶斯网络中弧的定向是指在已经有了变量之间的依赖关系图的条件下确定变量之间的边的方向的过程。介绍了一种改进了贝叶斯网弧定向的方法，该方法结合了目前多种定向方法的优点，实验证明该算法优于已存在的弧定向方法。

Bayesian networks can be used to express the probability distributions on the graph pattern, and provide a convenient method to express the probability information, and can express cause relations, but do not limit the cause relations. Bayesian networks have strong reasoning ability in solving nondeterministic problems, and attract more and more attentions from lots of researchers. Orienting arcs of the Bayesian network are part of the learning network, which means to determine the directions of the edges after getting the dependence structure of a Bayesian network.We introduce an improved algorithm about orienting edges which combines the advantages of some other algorithms. Experimental results show that this algorithm can effectively orient the edges of Bayesian networks.