The existing deep supervised hashing methods cannot effectively utilize the extracted convolution features, but also ignore the role of the similarity information distribution between data pairs on the hash network, resulting in insufficient discrimination between the learned hash codes. In order to solve this problem, a novel deep supervised hashing method called deep priority locally aggregated hashing (DPLAH) is proposed in this paper, which embeds the vector of locally aggregated descriptors (VLAD) into the hash network, so as to improve the ability of the hash network to express the similar data, and reduce the impact of similarity distribution skew on the hash network by imposing different weights on the data pairs. DPLAH experiment is carried out by using the Pytorch deep framework. The convolution features of the Resnet18 network model output are aggregated by using the NetVLAD layer, and the hash coding is learned by using the aggregated features. The image retrieval experiments on the CIFAR-10 and NUS-WIDE datasets show that the mean average precision (MAP) of DPLAH is 11 percentage points higher than that of non-deep hash learning algorithms using manual features and convolution neural network features, and the MAP of DPLAH is 2 percentage points higher than that of asymmetric deep supervised hashing method.