Key air route segments refer to route segments that play a hub-supporting role in the air transportation network of a specific airspace, and whose traffic anomalies (such as saturation or delay) are likely to trigger chain reactions through segment correlations. Identifying these segments and implementing resource allocation bias and targeted optimization for them can significantly improve network operational efficiency and alleviate delays. Current key segment identification methods only consider the physical connection status between individual air route segments, ignoring the spatiotemporal correlation of traffic changes among segments. To address this issue, this paper proposes a key segment identification method based on a segment-related network. Firstly, redundant search paths are reduced through the pruning of low-impact indicators, and an improved FP-Growth algorithm is proposed to analyze segment correlations. On this basis, a segment-related network is constructed, where segments serve as nodes and correlations between segments serve as edges. This network integrates the changing relationship between segment flows and the physical connections between segments, thereby improving the accuracy of identification. Finally, in the segment-related network of the Guangdong-Hong Kong-Macao Multi-Airport Terminal Area, key segments such as Pingzhou-Conghua, Pingzhou-Gaoyao, and Zhuhai-Nanlang are identified using degree centrality, PageRank, eigenvector centrality, and global centrality analysis methods.