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To improve the quality and utilization of unmanned aerial vehicle point cloud data, slope filtering technology is used to optimize the design of unmanned aerial vehicle point cloud feature extraction and filtering classification methods. Utilizing hardware devices to obtain drone surveying point cloud data, the initial point cloud data is registered through two steps: coarse registration and precise registration. By filtering and processing the point cloud data measured by drones, the interference terms in the initial data are reduced. Using slope filtering technology to extract terrain, texture, shape and other features from unmanned aerial vehicle (UAV) point cloud data, and based on the calculation of feature similarity, complete the filtering and classification of the surveyed point cloud. Through performance testing experiments, it was concluded that compared with traditional methods, the optimized design method resulted in a 41.22% improvement in the signal-to-noise ratio of point cloud data, an overall increase in feature extraction, a significant reduction in redundancy, and a 1.25% and 2.1% increase in classification recall and precision, respectively.