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The correct identification of traffic signs is a prerequisite for smart vehicles to regulate driving and road traffic safety. In order to solve the problem that the target image of the smart car is blurred and the resolution is low, resulting in low recognition accuracy and poor timeliness, a traffic sign recognition model based on cascading depth network is constructed. The model cascades the super-resolution processing network ESPCN and target detection. Identifying the network RFCN, the ESPCN network improves the resolution of the input captured image, achieves super-resolution processing for low-resolution images, and extracts global features of the image from the RFCN network to realize the detection and classification of traffic signs. Balanced sampling and multi-scale training strategies combined with data-enhanced pre-processing methods enhance the robustness and scalability of the network model. The experimental results show that the recognition rate of common traffic signs is 98.16%, the recall rate is 96.2%, and the robustness is good.