Focused on the issue that the traditional important node identification method for K-shell networks needs global topology during iteration and cannot be used in dynamic networks, an important node identification method for dynamic networks based on neighborhood priority asynchronous H operation was proposed. Firstly, the algorithm was proved to converge to Ks (K-shell) value; then the degree of each node was taken as the initial value of h-index, and the nodes to be updated were selected by the h-index ranking of the node and the h-index change of the neighbor nodes; meanwhile the h-index was modified to adapt to the topology change according to the number change and maximum degree of the dynamic network nodes, finally the algorithm converged to the Ks and the important nodes were found. The simulation results show that the algorithm can find important nodes effectively by local information of neighbor nodes with less convergence time. Compared with the random selection algorithm and the neighborhood-variety selection algorithm, the convergence time of the proposed algorithm decreases by 77.4% and 28.3% respectively in static networks and 84.3% and 38.8% respectively in dynamic networks.