There are a large number of complex polygons containing thousands of holes (or even nested holes) in the land cover/land use vector data, and the existing spatial data indexing method has failed to indicate the inclusion relationship between complex polygons and their holes, resulting in computationally heavy and inefficient processing such as spatial data conflict detection and updating. In order to solve this problem, an improved quadtree spatial index method with inclusion relations of the complex polygons is presented in this paper. The method classifies the polygons in the nodes into five types according to the way they intersect the axes in the corresponding quadrant of the quadtree, i.e., intersect only the X positive axis, intersect only the X negative axis, intersect only the Y positive axis, intersect only the Y negative axis, and intersect both X and Y axes, and stores each of these polygons in five sublists (buckets) in the corresponding hierarchical index nodes, and then stores the parent-child inclusion relationship between the polygons in the node polygon objects. The authors developed the spatial index structure with inclusion relations and the algorithms of the corresponding operations（e.g.，insert, delete and query）for the complex polygons. The effectiveness of the approach in this paper is verified by an experiment of land cover data incremental updating, experimental results show that the time efficiency of the incremental updating is increased about several times using the proposed index method than that of the traditional quadtree index, and the improvement in efficiency is more significant with increasing data volume.