Since the development of high-throughput technologies, we can capture large quantities of gene¡¦s expression data from DNA microarray data, so there are some technologies have been proposed to model gene regulatory networks. Gene regulatory networks is mainly used to express the relationship between the genes, but only can express a simple relationship, and can¡¦t clearly show how the operation between genes regulatory. In the simulation method of gene regulation, the mathematical methods are more often used. In the mathematical methods, S-system is the most widely used in non-linear differential equations. When the use of mathematical simulation of gene regulatory networks, there are mainly two aspects¡G(1) deciding on the model structure and (2) estimating the involved parameter values. However, when using S-system simulated the gene regulatory networks, we can only know the gene profiles, and there is no way to know the regulatory relationships between genes, but in order to understand the relationship between genes, we must clearly understand how genes work. Therefore, we propose to encode parameter values to infer the regulatory parameter values between genes. We propose the method of encoding parameter values, and using six artificial genetic datasets, and assuming 100% parameter values are known, 90% known, 70% known, 50% known, 30% known, 10% known. The experimental results show, besides it can infer a high proportion of non-regulation, positive regulation and negative regulation, also can infer more precise parameter values, and also has a clear understanding of the regulatory relationship between genes.