Improving the autonomous landing capability of unmanned aerial vehicles （UAVs） holds significant importance in enhancing their operational efficiency and survival ability in the field. This paper presents a novel approach utilizing onboard video for automatic detection of UAV landing zones， aiming to enhance the UAV’s autonomous obstacle avoidance and landing capabilities in the absence of prior scene knowledge. We integrate a deep learning network incorporating multi-view geometric constraint methods into the simultaneous localization and mapping （SLAM） algorithm， aiming to construct a three-dimensional map of the scene while actively identifying potential obstacles. Subsequently， we propose a landing area detection algorithm that takes into account factors such as landing area and flatness. By conducting spatial analysis on voxel grid maps， we can identify the landing area of UAVs. This algorithm utilizes spatial analysis on a voxel grid map to identify the suitable landing area for the UAV. Experimental evaluation is conducted in various scenarios， demonstrating the accuracy of the proposed approach.