In order to solve the problem of difficulty in balancing real-time and accuracy of lane detection in complex scenes such as shadows and nights using traditional image processing methods, a fast lane detection algorithm based on feature fusion and anchor point classification is proposed to meet the needs of real-time traffic scenes. In the image preprocessing stage, the image is divided into grid like row anchors, and lane detection is transformed into a row anchor classification problem, significantly reducing computational complexity. The lane detection network adopts ResNet-18 as the backbone network and introduces an aggregation module to enhance context feature extraction and improve the ability to capture lane structure information. Combining feature pyramid network (FPN) to achieve multi-scale feature fusion and complement local and global features of lane markings. In addition, an auxiliary segmentation branch with ASPP module is introduced to further optimize the accuracy of lane detection. Experiments were conducted on the public datasets TuSimple and CULane, and the accuracy on the TuSimple dataset reached 96.16%, with a running time of only 3.2 ms; Obtained 70.3% F1 score and FPS of 310 fps on the CULane dataset. The experimental results show that the proposed method significantly improves detection speed while ensuring detection accuracy.