The invention relates to a high dimensional data index method based on maximum clearance space mapping, and belongs to the database field, comprising following steps: a step 1 of processing the maximum clearance space mapping to calculate each dimensional clearance value of a given data space, and selecting values before K with larger dimensional clearance values, and projecting actual data points of the given data space into K dimensional spaces; a step 2 of manufacturing MS-tree Ms-tree, namely firstly finding a suitable knot insertion M, wherein, if the knot insertion is not full, the object is directly inserted into the knot insertion, and if the knot insertion is full, the knot insertion is broken up, then checking if the insert object in MBR of the knot insertion M or not, wherein, if not, then updating the MBR of the knot insertion M and mapping original space into a low dimensional space; a step 3 of processing a similarity query. The invention has an advantage of improving query performance via reducing visit of false activity subtree, so as to reduce visit times of the false activity subtree to improve the performance of index similarity query.