Aiming at the problem of high difficulty and low accuracy in road traffic sign recognition under haze weather, a traffic sign recognition model based on YOLOv5 was proposed. Firstly, the convolutional attention mechanism was integrated into the original YOLOv5 model to enhance features in the spatial dimension and channel dimension to suppress the interference of haze weather on the model. Then, BiFPN is used as the feature fusion structure in neck layer to more fully fuse multi-scale features and reduce the loss of target information. CIoU is used as the loss function of YOLOv5 to improve the positioning ability. K-means clustering algorithm was used to re-obtain anchor frame values in TT100K and CODA datasets to accelerate the convergence speed of the model. The experimental results show that the recognition accuracy of the improved model reaches 92.5%, which is 5.6% higher than that of YOLOv5, and it can still accurately identify traffic signs in haze weather. and the speed can reach 27 FPS, which can be used for real-time detection.