Approximate membership query method based on high-dimensional data filter

The invention discloses an approximate membership query method based on a high-dimensional data filter. Through definition of a new structure supported by a new locality-sensitive hashing function, multi-dimensional data in a target data set and multi-dimensional data to be queried are represented respectively, and the filter is not required to be reconstructed, so that approximate membership query using more filtering distance parameters can be supported, the space cost is significantly reduced, and integer multiple-distance data filtering is supported. 本发明公开了种基于高维数据过滤器的近似成员查询方法，通过定义新的距离敏感哈希函数支持的新结构，分别来表征目标数据集合中的多维数据和待查询的多维数据，不需要重新构造过滤器，能够支持更多的过滤距离参数的近似成员查询，大幅度减少了空间代价，支持整数倍距离的数据过滤。