The diversity existed in most recently hashing methods leads to the binary codes cannot efficiently preserve the data similarity. This paper, taking the ensemble learning theory and the parallel algorithm as a support, proposes a novel hashing method, i.e. Unsupervised Ensemble Hashing Learning (UEH). Firstly, the ensemble method is utilized to balance the diversity so as to reduce the quantization error. Specially, the higher accuracy and the larger diversity the base learner has, the more effective the ensemble method is. Then the bootstrap aggregating (bagging) method is used to increase the diversity. Finally, the paper uses iterative quantization to guarantee equivalent information of each hashing bits to effectively enhance the generalization ability. The paper validates the method on two large scale datasets CIFAR-10 and MINIST for image retrieval, and the experimental results show that the performance gains of the proposed method is improved by 6%~15% compared with the stateoftheart methods. In addition, an important benefit of bagging scheme for hashing is inherently favorable to parallel computing