High repetition rate of query results is a problem needing a prompt solution in top-k query processing. Existing solutions require the traversing over all objects in initial result set which may cause a lower efficiency in query processing. To address the issue, this paper first maps initial result set to the Euclidean space and selects the optimal subspace using either the score-based method or distance-based method by adopting the pulling strategy. Applying the distance-based method, the Euclidean space is partitioned and the number of second query objects is reduced by incorporating geometric properties of Voronoi diagram. Further, the bounded diversification algorithm over top-k query results is developed and the soundness of the algorithm is proved. Experimental results demonstrate that the proposed algorithm improves the efficiency of top-k query processing.