With the development of mobile Internet technology， the scale of image data is getting larger and larger， and the large-scale image retrieval task has become an urgent problem. Due to the fast retrieval speed and very low storage consumption， the hashing algorithm has received extensive attention from researchers. Deep learning based hashing algorithms need a certain amount of high-quality training data to train the model to improve the retrieval performance. However， the existing hashing methods usually ignore the problem of imbalance of data categories in the dataset， which may reduce the retrieval performance. Aiming at this problem， a deep hashing retrieval algorithm based on meta-learning network was proposed， which can automatically learn the weighting function directly from the data. The weighting function is a Multi-Layer Perceptron （MLP） with only one hidden layer. Under the guidance of a small amount of unbiased meta data， the parameters of the weighting function were able to be optimized and updated simultaneously with the parameters during model training process. The updating equations of the meta-learning network parameters were able to be explained as： increasing the weights of samples which are consistent with the meta-learning data， and reducing the weights of samples which are not consistent with the meta-learning data. The impact of imbalanced data on image retrieval was able to be effectively reduced and the robustness of the model was able to be improved through the deep hashing retrieval algorithm based on meta-learning network. A large number of experiments were conducted on widely used benchmark datasets such as CIFAR-10. The results show that the mean Average Precision （mAP） of the hashing algorithm based on meta-learning network is the highest with large imbalanced rate；especially， under the condition of imbalanced ratio=200， the mAP of the proposed algorithm is 0.54 percentage points，30.93 percentage points and 48.43 percentage points higher than those of central similarity quantization algorithm， Asymmetric Deep Supervised Hashing （ADSH） algorithm and Fast Scalable Supervised Hashing （FSSH） algorithm.