Hashing is an important method for fast neighbor search on large scale dataset in Hamming space. While most research on hash models are focusing on single-view data, recently the multi-view approaches with a majority of unsupervised multi-view hash models have been considered. Despite of existence of millions of unlabeled data samples, it is believed that labeling a handful of data will remarkably improve the searching performance. In this paper, we propose a semi-supervised multi-view hash model. Besides incorporating a portion of label information into the model, the proposed multi-view model differs from existing multi-view hash models in three-fold: 1) a composite discrete hash learning modeling that is able to minimize the loss jointly on multi-view features when using relaxation on learning hashing codes; 2) exploring statistically uncorrelated multi-view features for generating hash codes; and 3) a composite locality preserving modeling for locally compact coding. Extensive experiments have been conducted to show the effectiveness of the proposed semi-supervised multi-view hash model as compared with related multi-view hash models and semi-supervised hash models.