The existing traffic sign recognition technology has the problems of high recognition rate, high power consumption or low recognition rate and low power consumption. Aiming at this problem, a new light WACNN of recognition algorithm is constructed. Firstly, six layers convolutional neural network are constructed by using TensorFlow, in which the first three layers are convolutional pooling layers, the fourth layer is 1×1 convolutional layer, the fifth layer is fully connected layer, the sixth layer is output layer, and the first four layers are then added batch normalization method. Secondly, histogram equalization is used to preprocess traffic sign images. Finally, the model is tested on GTSR. The experimental results show that the proposed model not only greatly shortens the training time, but also the recognition accuracy can reach 97%.