Bridging nodes are critical for maintaining information, material, and energy exchanges throughout a complex network. However, the importance of bridging nodes has often been ignored in previous studies, which have instead focused on hub nodes. Here, we propose a novel approach named Bridging Node Centrality (BNC) to identify bridging nodes. BNC is a method based on different levels of network paths, and it combines traffic flow and positional properties of nodes, which greatly diminishes the effect of node degree. The performance of BNC was tested in many synthetic and real-world networks including LFR benchmark networks, social networks, biological networks, collaboration networks, etc. By comparing with other methods, and the results indicated that whether based on accuracy or approximate accuracy, BNC could be accurate and robust all the time in different types of complex networks.