To address the problems of insufficient learning of semantic information between image data and the need to retrain the model every time when the hash code length is changed in traditional unsupervised hash image retrieval model, an unsupervised search framework for large-scale image dataset retrieval, the unsupervised parallel hash image retrieval model based on correlation distance, was proposed. First, the Convolutional Neural Network (CNN) was used to learn the high-dimensional feature continuous variables of the image. Second, the pseudo-label matrix was constructed by using the correlation distance measure feature variables, and the hash function was combined with deep learning. Finally, the parallel method was used to gradually approximate the original visual characteristics during the hash code generation, realizing the purpose of generating the multi-length hash codes in one training. Experimental results show that the mean Average Precisions (mAPs) of the proposed model for four of 16 bit, 32 bit, 48 bit and 64 bits hash codes on FLICKR25K dataset are 0.726, 0.736, 0.738, 0.738,respectively, which are 9.4, 8.2, 6.2, 7.3 percentage points higher than those of Semantic Structure-based Unsupervised Deep Hashing (SSDH) model, respectively; and compared with SSDH model, the training time of the proposed model is reduced by 6.6 hours. It can be seen that the proposed model can effectively shorten the training time and improve the retrieval accuracy in large-scale image retrieval.