Abstract: With the advent of the era of big data， the information data on the Internet is growing exponentially. Among these data， image resource accounts for a very large proportion， so how to carry out accurate and efficient image retrieval from massive images has become one of the important research topics today. At present， there are some problems in large-scale image retrieval， such as poor retrieval performance and low accuracy due to the inability to effectively focus on the key areas of the image. Based on the above shortcomings， an asymmetric deep hash algorithm that integrates the attention mechanism is proposed， which is modified based on convolutional neural network. The existing mixed attention mechanism guided by semantic features is improved and embedded into the network， so that the hash network can analyze the global semantic information and local semantic information together. At the same time， a new quantization function is designed to reduce quantization error， so as to enhance the feature expression ability of hash coding. This method is compared with other hashing methods on the CIFAR-10 and NUS-WIDE datasets with evaluation standard mAP. The results show that the proposed network model can combine global and local spatial features well， and improve the image retrieval performance.

Key words: image retrieval, attention mechanism, deep hashing, convolutional neural network, feature extraction