Abstract: Geological disaster investigations enable timely detection of hazards, issuance of early warnings, and prevention of loss of life and property. To address the challenges of high risk and low efficiency of high steep slopes investigation, this study proposes a method of three-dimensional reconstruction and structural plane identification of high steep slope based on UAV close-range photogrammetry. Using Zengziyan in Nanchuan, Chongqing as a case study, the process begins with acquiring high-definition aerial photographs through UAV close-range and supplemental route photogrammetry. The SFM-MVS algorithm is utilized to construct detailed 3D models and point clouds. An adaptive KNN algorithm is introduced to enhance the coplanarity detection passing rate in point clouds, while optimal planar equations are fitted using the least squares method. Point cloud clustering is achieved using a genetic annealing fuzzy C algorithm. Finally, according to the point cloud covariance matrix eigenvalues and eigenvectors, the point cloud plane parameters and normal vectors are inverted, and the structural surface identification and structural surface yield parameters extraction are completed. The results indicate a 99.6% passing rate for point cloud coplanarity detection, with a maximum deviation in identified orientation parameters of only 4.82°. This research provide insights for rapid acquisition of geological information, stability evaluation, and disaster prevention and mitigation for high steep slopes.