为提高公交网络运营稳定性，保障乘客顺利出行，需准确识别公交网络中的重要节点并进行重点保护。鉴于此，考虑公交网络节点相互影响的网络拓扑特性和现实特征，提出了DeB（Degree and edge Betweenness）节点重要性识别算法，以边介数和客流表示节点相互影响力的大小，同时，引入节点n 阶吸引度作为衡量节点重要性的指标。最后，以宁波市公交网络为例，研究比较了在基于DeB算法、度中心性算法、介数中心性算法的模拟蓄意攻击下，网络效率和最大连通子图的变化情况，以验证算法的有效性和精确性。结果显示，基于DeB算法得到的节点1阶吸引度的蓄意攻击对网络效率和最大连通子图的影响最大，即对重要节点识别的精确性越高，且节点吸引度阶数越高，算法精确性越低；与度中心性、介数中心性相比，节点1阶吸引度的精确性更高，表明DeB算法得到的节点1阶吸引度可更准确地衡量节点的重要性。

In order to improve the operational stability of public transportation network and ensure the smooth travel of passengers, it is necessary to accurately identify important nodes in the public transportation network and carry out key protection. In view of this, the DeB(Degree and edge Betweenness) node importance recognition algorithm was proposed considering the network topology and practical characteristics of the mutual influence of public transportation network nodes. The DeB algorithm represents the mutual influence of nodes by the edge betweenness and passenger flow. At the same time, n-order attractiveness of node was introduced as an indicator to measure the importance of nodes. Finally, taking Ningbo public transportation network as an example, the changes in network efficiency and largest connected subgraph under simulate deliberate attacks based on DeB algorithm, degree centrality, and betweenness centrality were compared to verify the effectiveness and accuracy of the algorithm. The results show that the deliberate attacks based on the DeB algorithm′s 1-order attractiveness of node has the greatest impact on network efficiency and the largest connected subgraph, which means that, the higher the accuracy of identifying important nodes and the order of node attractiveness are, the lower the accuracy of the algorithm is. Compared with degree centrality and betweenness centrality, the accuracy of node 1-order attractiveness is higher, indicating that the 1-order attractiveness of node obtained by the DeB algorithm can measure the importance of nodes more accurately.