With the increasing amount of data being collected, developing fast indexing methods with high accuracy becomes important for information retrieval tasks. To address this issue, this paper proposes an indexing method based on hashing mechanism with subspace learning. Firstly, the subspace is learned on a set of labeled data. To guarantee the locality preserving characteristics in the original space for the samples with similar semantic labels, the distances between the nearest neighbors are computed to measure the intra-class scatter. Besides, the distances between the centers of samples with dissimilar semantic labels are also computed to measure the inter-class scatter in order to enhance the discriminative power of the codes. The projections of the hash functions are then learned by relaxing the constraint of the formula. The biases are further learned based on the projections. Finally, the proposed method is evaluated on the datasets MNIST and CIFAR-10 to compare with the state-of-the-art methods. Experimental results show that the proposed method achieves significant performance and high effectiveness in searching semantically similar neighbors.