We propose a mine ground point cloud extraction algorithm that combines statistical filtering and density clustering to effectively extract ground point clouds and improve the operational efficiency. First, we improve the statistical features based on an efficient KD-tree index algorithm and statistical features, and analyze the spatial distribution characteristics of non-ground points. We then cluster the density space and extract the ground points based on the distribution characteristics of two-dimensional characteristic density space. Lastly, the effective ground points are obtained by intersecting the extracted results of each density space, and the algorithm complexity is observed to be o(n2). Experiments demonstrate that the proposed algorithm has high extraction accuracy and efficiency. The test indicates that when the neighborhood point value is 36, the effect is the best, with a total error of 0.00770 and a mean square error of 0.019633. Meanwhile, the extraction and calculation time of 510519 points are less than 27 s, which is approximately 1/7 of the time required by traditional methods. In addition, we select a large-area mine point cloud to verify the universality of the algorithm.