Aiming at the shortcomings of convolutional neural network in the recognition of traffic signs that the real-time performance is not good，and the equipment hardware requirements are too high，a real-time and high-precision improved network based on lightweight convolutional neural network is proposed. Separate convolution and activation function Mish，speed up the network training and recognition speed，reduce the requirements for hardware equipment；on the other hand，through the improvement of the network architecture and level，while reasonably changing the size and number of convolution kernels，the expression of image features and transfer. The experimental results on the BelgiumTSC traffic sign dataset show that the improved network significantly increases the network training speed，and the recognition accuracy is slightly higher than the original network，which verifies the effectiveness of the method in this paper. Compared with other models，this model can complete the task of traffic sign recognition more quickly and accurately，which verifies the feasibility of this method.