Abstract: Bayesian Network is an important probabilistic graphical model that represents a set of random nodes and their conditional dependencies. Bayesian Networks have been increasingly used in a wide range of applications, such as natural language processing, hardware diagnostics, bioinformatics, statistical physics, econometrics, etc. Learning structure from data is one of the most important fundamental tasks of Bayesian network research. Particularly, learning optional structure of Bayesian network is a Non-deterministic Polynomial-time (NP) hard problem. To solve this problem, many heuristic algorithms have been proposed, some of them learn Bayesian network structure with the help of different types of prior knowledge. However, the existing algorithms have some restrictions on the prior knowledge, such as quality restriction of prior knowledge, use restriction of prior knowledge, etc. This makes it difficult to use the prior knowledge well in these algorithms. In this paper, we introduce the prior knowledge into Markov Chain Monte Carlo (MCMC) algorithm and proposed an algorithm called Constrained MCMC (C-MCMC) algorithm to learn the structure of the Bayesian network. Three types of prior knowledge are defined: existence of parent node, absence of parent node and distribution knowledge including the Conditional Probability Distribution (CPD) of edges and the Probability Distribution (PD) of nodes. All of these types of prior knowledge are easily used in this algorithm. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC. We conduct extensive experiments to demonstrate the feasibility and effectiveness of the proposed method C-MCMC.