The nature of causal network orientation problems is a “many-to-many” causal discovery process. The traditional V-structure method can only determine a set of Markov equivalent classes rather than the final causal relationship. In order to solve this problem, based on the Kolmogorov complexity, a causal orientation rule of local networks is deduced using the Bayesian chain rule, thus a high-dimensional global network causal orientation rule is proposed on this basis. At the same time, the former is used to improve the Markov blanket typical algorithm based on the local condition independent information searching; the latter is used to improve the constraint based causal network structure learning typical algorithm. The experimental results show that the improved algorithm can effectively improve the execution efficiency while ensuring high accuracy.