Objective Estimating the depth information to obtain the distance between the photographed object and the camera is the method to obtain the depth information in monocular vision SLAM. As unsupervised monocular depth estimation algorithms suffer from insufficient accuracy as well as large errors a continuous frame depth estimation network based on a hybrid attention mechanism with multi-scale feature fusion was proposed. Methods Information on depth and 6 degrees of freedom of pose were obtained by two encoder-decoder structures for depth estimation and pose estimation respectively. The depth information and the pose information were used for image reconstruction with the original image loss calculation to output the depth information. The decoder encoder structure for depth estimation formed a U-shaped network and the same encoder was used for both the pose estimation network and the depth estimation network and the pose information was output through the pose estimation decoder. The feature maps at four different scales were extracted in the encoder using a hybrid attention mechanism CBAM network combined with a ResNet network. For the enhancement of the estimated depth information contour details the extracted features of each different scale were then assigned learnable weight coefficients to extract local and global features and then fused with the original features. Results Evaluation of error and accuracy was performed on the KITTI dataset and finally testing was also performed. Compared with the classical monodepth2 monocular method the relative error root mean square error and log root mean square error in the error evaluation metrics were reduced by 0. 034 0. 129 and 0. 002 respectively and self-made test images demonstrated the generalizability of the network. Conclusion The multiscale features are extracted using a ResNet network combined with a hybrid attention mechanism while multiscale feature fusion on the extracted features enhances the depth estimation and improves the contour details.