To solve the UNet3+ network with depth deepening a large number of fusion redundant operation that the model training time is too long and resulting in road extraction using less problems ,the UNet3+ network improve-ment ,by cutting UNet3 + network hierarchy using Bottleneck module to replace the convolution layer in the original network ,retain the network feature fusion ability and reduce the network complexity ,and introduce hybrid attention mechanism optimization model ,build a new network model. The improvement method is compared with several typical road extraction models. The experimental results show that : (1) compared with Unet3+ network ,the proposed method improves by 4. 72% ,2. 46% ,4. 84% and 2. 01% respectively ,all better than the comparison algorithm; ( 2) com- pared with several classical feature extraction models ,the improved model has better recognition effect ,and phenoty- ping in the accuracy ,connectivity ,integrity and other aspects of road extraction.