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A medical image retrieval method combining attention mechanism is proposed for a series of problems such as poor retrieval performance,low accuracy and lack of interpretability in current medical image retrieval.Based on deep convolutional neural networks and taking Bayesian models as the framework,the proposed algorithm introduces an attention mechanism module guided by semantic features.Local feature descriptors containing semantic information are generated under the guidance of the classification network.Both global features and local features rich in semantic information are used as inputs to the hash network,which enhances the feature representation capability of hash coding by guiding the hash network to pay attention to important feature regions from both global and local perspectives.And the weighted likelihood estimation function is introduced to solve the problem of the unbalanced number of positive and negative sample pairs.MAP and NDCG are used as evaluation metrics,and the ChestX-ray14 dataset is selected for experiments.The proposed algorithm is compared with the current commonly used deep ha-shing methods.Experiment results show that the MAP and NDCG values are much better than the existing deep hashing methods at different code levels of hash coding,which proves the effectiveness of the proposed algorithm.