Abstract: To reduce the unnecessary waste of controlling resources and improve the convergence rate in the apron multi-agent network scene, a novel consensus algorithm based on the self-triggered impulsive pinning control was proposed. A multi-agent network model was established based on graph theory, and an impulse excitation function was designed to estimate each pulse moment automatically. The meliorative node importance measure with more evaluation indexes was defined simultaneously to select the more appropriate pinning nodes. The convergence of network status was simulated and analyzed in MATLAB. The results show that by the proposed method, the sampling trigger frequency and the controlled nodes number are effectively reduced, and the better performance than competing methods can be obtained with the smoother trend of convergence. The simpler the topological structure is, the better the control effect is.