To address the low recognition accuracy resulting from multiple scale changes in the traffic signs of complex scenes， an improved YOLOv4 algorithm is proposed. First， an attention-driven scale-aware feature extraction module is designed， and the range of receptive fields in each layer is widened to obtain more fine-grained multi-scale features by constructing a hierarchical connection mode similar to the residual structure； this is followed by the generation of a pair of attention maps with directional-aware and position-sensitive characteristics under the attention drive so that the network can focus on key areas with more discrimination. Following this， a feature-aligned pyramid convolution feature fusion module is constructed， and the feature offset between adjacent scale feature maps is obtained via convolution for feature alignment. Finally， the network adaptively learns the optimal feature fusion mode through pyramid convolution and constructs a feature pyramid to identify traffic signs with different scales. Experimental results indicate that the recognition accuracy for the TT100K dataset is improved by 5.4% compared with that of the original YOLOv4 algorithm， which is superior to other recognition algorithms， and the FPS reaches 33.17. Thus， the proposed algorithm satisfies the requirements of accuracy and real-time performance for road traffic sign recognition.