Abstract: To address the problems that convolutional neural networks are weak in extracting features of multi-label remote sensing images and the reflection of complexity multiple labels in remote sensing images, a multi-label remote sensing image retrieval method based on attention mechanism and soft matching is proposed. In the feature extraction stage, the method was based on the densely connected convolutional neural networks, and a CBAM (Convolutional Block Attention Module) layer was added after each dense block to achieve feature extraction of multi-label image regions. During model training, the joint loss function that distinguished hard matching and soft matching was used to learn the Hash code representation of the images. The retrieval results were obtained by evaluating the Hamming distance between the image Hash code and the retrieved image Hash code. The experimental results show the proposed method has a significant improvement in retrieval accuracy compared with other deep hashing methods based on global features on the universal dataset NUS-WIDE and multi-label remote sensing image dataset DLRSD.