Dynamic gene regulatory network is a complex network representing the dynamic interactions between genes in organism. The interactions can be divided into two groups, motivation and inhibition. The researches on the evolution of dynamic gene regulatory network can be used to predict the gene regulation relationship in the future, thus playing a reference role in diagnosis and prediction of diseases, Pharma projects, and biological experiments. However, the evolution of gene regulatory network is a huge and complex system in real world, the researches about its evolutionary mechanism only focus on statics networks but ignore dynamic networks as well as ignore the types of interaction. In response to these defects, a dynamic gene regulatory network evolution analyzing method (DGNE) is proposed to extend the research to the field of dynamic signed networks. According to the link prediction algorithm based on motif transfer probability (MT) and symbol discrimination algorithm based on latent space character included in DGNE, the evolution mechanism of dynamic gene regulatory network can be dynamically captured as well as the links of gene regulatory network are predicted precisely. The experiment results showed that the proposed DGNE method performs greatly on simulated datasets and real datasets.