Dynamic graph link prediction aims to predict the formation or disappearance of links between nodes in a graph based on their historical interactions. To address the issue of high energy consumption associated with modeling dynamic networks using recurrent neural networks at fine-grained temporal graphs, a dynamic graph link prediction model optimized by spiking neural networks was proposed. By the node memory updater incorporated spiking neural networks and the spiking update process of node memory, the evolving dynamics of dynamic graphs were learned by graph neural networks and the model achieved link prediction. The results on three publicly available classic datasets show that the proposed model exhibits improved runtime efficiency while maintaining accuracy, showcasing favorable performance in dynamic graph link prediction tasks.