For transcriptional kinetic mechanism research, raising the accuracy of identifying model parameters is an important work. Usually, the training data we obtained are noisy and sparse. This paper uses the ordinary differential equation to model transcriptional regulation. Gaussian prior distribution is adopted to describe the unobserved transcriptional factors activity. The approximation of gene expression profiles together with their time derivatives are considered during the inference. Gaussian Process helps to smooth and denoise experimental data and get their derivatives. Cultural genetic algorithm is utilized to optimize kinetic parameters in the model and hyper-parameters in kernel function. Experimental results on simulated data and real data show that the proposed method can improve the accuracy of identification and make the model fit data better.