A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive.In this paper,we propose the L-priorities Bloom filter (LPBF) as a new member of the Bloom filter (BF) family,it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set.We demonstrate the time and space complexity,especially the false positive rate of LPBF.Furthermore,we also present a detailed practical evaluation of the false positive rate achieved by LPBF.The results show that LPBF performs better than standard BFs with respect to false positive rate.