The safety and stability of autonomous vehicle driving are inseparable from accurate lane recognition. However, daily driving faces challenges such as complex and changing weather and lighting conditions, blurred or blocked road markings. Research and design lane line recognition algorithms based on deep neural networks to improve the robustness of recognition technology in complex environments and the accuracy of detection results. By constructing a fully convolutional neural network model with VGG-16 as the main chain and embedding channel attention and spatial attention mechanisms, end-to-end pixel-level lane lines semantic segmentation is achieved. The new model embedding the attention module is verified on the CULane general data set. Compared with the VGG-decoding semantic segmentation method, its average pixel accuracy and Mean Intersection over Union (MIoU) increased by 2.2% and 1.3% respectively. And in the scenario where lanes do not exist, the pixel accuracy of the prediction results reaches 70%. Research on image segmentation algorithms embedding attention mechanisms provides an effective solution to the problem of lane line recognition, and strongly supports the application of lane line detection technology in driverless driving scenarios.