In order to improve the feature extraction ability of the network in complex environments, a lane line recognition method for double feature extraction network is proposed. Firstly, a double feature extraction network is constructed to reduce the loss of detailed semantic information and enhance the recognition ability of the model in com- plex environments. Then, the improved atrous spatial pyramid pooling structure is used to increase the receptive field and extract more rich contextual information. In addition, depthwise separable convolutions are combined to reduce the computational complexity of the model. Finally, a channel attention module is constructed to focus on feature channels with more effective information. Experimental results show that the proposed method achieves an accuracy of 97. 7% and an mIoU of 76. 2% on the Tusimple dataset, with a single image recognition time of 26. 24 ms. When recognizing lane lines in complex environments, the proposed method demonstrates good robustness.