In UAV photogrammetry, traditional ground point cloud extraction methods have poor adaptability when extracting roads from image point cloud data. Therefore, this study proposes a UAV photogrammetric point cloud road adaptive extraction method. Firstly, the point cloud is divided into three categories based on its spatial geometric characteristics. Then, corresponding methods are applied to remove non-road point cloud categories. Finally, the point cloud data obtained through the adaptive extraction method is filtered for smoothing and subjected to color-based region growing segmentation. Experimental results show that the I-class error of road point cloud extracted by this method is 4.97%, and the II-class error is 1.14%. This method effectively extracts target road surfaces, improving the efficiency of point cloud data processing in UAV photogrammetric applications.