地震易致建筑损毁与人员伤亡，卫星遥感获取的数据精准度不足，无人机凭借快速响应、高机动性与搭载设备的精准数据采集能力，成为震后救援信息搜寻的关键工具。针对当前无人机搜寻航线依赖人工经验、采集效率低等问题，开展震后无人机快速搜寻航线规划方法研究。提出“高价值兴趣点（Point of Interest POI）筛选-空间聚类分区-救援优先级评估-蚁群算法路径优化”四阶段技术框架，筛选六类高价值POI数据，使用双层聚类算法分区并核算无人机数量，构建多指标优先级模型，引入蚁群算法优化航线。以唐山市丰南区为研究区，选取2024年12月六类共1443个高价值POI，用大疆M300RTK无人机实验，双层聚类算法使空白区域减少61.6%，针对分区0-4生成4条符合约束的航线，较甘肃积石山近似面积案例减少13架次。该方法能生成高效无人机飞行路线，为震后无人机救援信息搜寻工作提供支撑。

Earthquakes are prone to causing building damage and casualties. While satellite remote sensing fails to deliver sufficient data accuracy, unmanned aerial vehicles (UAVs) have emerged as a pivotal tool for post-earthquake rescue information search, leveraging their rapid response, high mobility, and the precise data collection capability of onboard equipment. To address the existing issues such as over-reliance on manual experience for UAV search routes and low data collection efficiency, this study develops a rapid search route planning method for post-earthquake UAVs. A four-stage technical framework is proposed: "high-value POI screening - spatial clustering zoning - rescue priority evaluation - ant colony algorithm-based path optimization". Six categories of high-value POI data are selected, a two-layer clustering algorithm is employed for zoning and calculating the required number of UAVs, a multi-index priority model is constructed, and the ant colony algorithm is introduced to optimize routes. Taking Fengnan District of Tangshan City as the research area, 1,443 high-value POIs across six categories from December 2024 are selected for experiments using DJI M300RTK UAVs. The results show that the two-layer clustering algorithm reduces blank areas by 61.6%, and 4 constraint-compliant routes are generated for Zones 0-4. Compared with the case of similar area in Jishishan, Gansu Province, the number of flight sorties is reduced by 13. This method can generate efficient UAV flight routes and provide robust support for post-earthquake UAV rescue information search operations.