空间分块策略是K近邻搜索算法研究中的有效方法，然而现有算法进行空间划分时给出的子立方体大小主要取决于K值的大小，K值变化时需重新进行空间划分，影响了时间效率和稳定性。利用空间分块策略的优点，提出一种以建立离散数据空间索引为空间划分目标的K近邻搜索新算法。该算法预先对空间包围盒进行微分块，形成的子立方体结构仅与离散数据和预设参数相关，同一点云数据只需进行一次空间分配。搜索过程中，以计算点为球心建立空间动态球，判定符合条件的子立方体，进行K近邻搜索。测试结果表明，新算法较现有算法点云分配和遍历时间效率、随机点搜索时间稳定性及对不同K值的适应性等方面更具有优势。

Space block strategy is the effective method in finding K-nearest neighbors.However，during dividing the min-max box of the dataset，the size of sub-cubes mainly is decided by the K value，and the min-max box is needed to divide again along with the change of K value in existing algorithms，it has affected time efficiency and stability of algorithm.Using the advantages of space block strategy，a new algorithm for finding K-nearest neighbors is presented with establishing the space index of scattered points as the spatial division goal.The min-max box of the dataset is divided into a set of uniform sub-cubes in advanced，the structure of sub-cubes is only related to the scattered points and the default parameter，the same points cloud data just is distributed one time.In the course of searching，the dynamic sphere is builded using the test point as the center of sphere，and judging eligible sub-cubes in order to find K-nearest neighbors.The experimental results show that the new algorithm is more superiority than existing algorithms in points cloud distribution and searching time efficiency，the stability of searching time of random points，and the adaptability of different K value.