Existing deep learning-based hashing methods for image retrieval usually cascade several fully connected layers as the hash coding layer and output each bit of the hash code in parallel. This approach treats hash encoding as the information encoding of images and ignores the relevance between bits of the hash code in the coding process and the redundancy of coding, which leads to the limited encoding performance of networks. In light of the principle of code check, this study proposes SHNet, a deep hashing method based on serial encoding. Different from the traditional hashing method, SHNet designs the hash coding network layer structure as a serial mode and verifies the first part of the serial hash codes in the process of generating hash codes, so as to make full use of the relevance and redundancy of codes to generate more informative, more compact, and more discriminative hash codes. Using mAP as the evaluation standard of retrieval performance, the study compares the proposed method with current mainstream hashing methods. The results show that the mAP values of the proposed method under different hash coding lengths are superior to those of the current mainstream deep hashing algorithm on the three datasets of CIFAR-10, ImageNet, and NUS-wide, which proves its effectiveness.