Many nearest neighbor query algorithms often fail to meet the query requirements on big data due to their high time and space cost. Hash query technology can significantly reduce not only query time

but also required storage cost. The main principle is to map the high-dimensional data into a set of binary codes with locality preserved. However

most existing hashing methods do not consider the weight differences between the binary bits when calculating the Hamming distances between those binary codes from data. Generally

different hashing bits may contain different amount of information. Focusing on the above issue

this paper proposes WQ (Weighted Quantization) that will assign different weights for each bit of the binary code

as well as a corresponding quantization method. Experimental results show that WQ algorithm has superior performance of data retrieval compared with several other hashing methods.