Lane detection is one of the core technologies of assisted driving and automatic driving. To further enhance the ability of lane feature extraction, a lane line recognition algorithm based on deep learning and recurrent multi-feature information aggregator was proposed. Given the problem of model operation speed, the algorithm took the lane detection problem as a classification problem based on row selection cells. Given the lane information aggregation problem in the image, a novel RMFA (recurrent multi-feature information aggregator) method was proposed. The ResNet (residual neural network) was combined with the method to propose a lane line recognition network ResNet-RMFA fused with contextual and multi-channel information. The performance of the network model was tested on the open datasets of Tusimple and CULane. The experimental results show that the reasoning time of a single frame image of the model can reach 4.8 ms, the accuracy on the Tusimple dataset is 96.07%, and the F₁ score on the CULane dataset (IoU = 0.5) is 69.3%, achieving a good balance between speed and accuracy.