Inferring the gene regulatory networks (GRNs) structure is the research basis of functional genomics. GRNs can help to understand the regulatory mechanism among genes, exploring the essence of complex life system. Traditional Bayesian network methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. By using the ordered conditional mutual information (CMI) and limited parent node genes, in this work, we present a novel algorithm (namely OCMIPN) to fast infer GRNs from gene expression data. OCMIPN first uses ordered conditional mutual information to construct an initial GRN relation network. Then, according to the priori knowledge of gene regulatory network topology structure, BN method is employed to generate final GRNs by limiting the number of parent nodes for each gene, which significantly reduces the computational complexity. Tested on the synthetic networks as well as real biological molecular networks with different sizes and topologies, the results show that OCMIPN can infer RGNs with higher accuracy and low computational times. The OCMIPN’s performance outperforms other state-of-the-art methods, such as LASSO, ARACNE, ScanBMA and LBN.