In order to address the urgent needs of dynamic regulation and emergency management in airport networks, a method for identifying key nodes in airport temporal networks based on a combined weighting-TOPSIS model was proposed. First, wavelet analysis was used to reveal the periodic characteristics of the airport network and construct a multi-layer temporal network model. Then, a comprehensive evaluation system was established by integrating temporal degree centrality, betweenness centrality, and other indicators. An AHP-entropy weight hybrid strategy was adopted to balance subjective and objective weight assignments. Finally, the TOPSIS method was introduced to rank node importance, and deliberate attack experiments are conducted to observe the impact of node removal on network structure and performance. Experimental results show that in the early stage of attacks, when 30% of the nodes are attacked, the size of the largest connected component drops by 14.0%, higher than the average of 8.2% under traditional methods. When the attack ratio reaches 30%~50%, network efficiency decreases by 91%, improving attack effectiveness by 46.1% compared to single-indicator methods. When 80% of nodes are removed, the residual network efficiency falls to 0.027, significantly lower than that achieved by other methods. This approach offers both theoretical innovation and practical feasibility for airport network resilience assessment, dynamic scheduling optimization, and emergency management in response to unexpected events.