Abstract: This paper designed a digital circuit of spiking convolutional neural network for recognizing handwriting number. Spiking neurons are used to replace the convolution kernels in CNN. Corresponding circuit structures are designed for convolution layer and pooling layer respectively to achieve full pipeline parallelism. Compared with the traditional convolutional neural network, when recognizing the MNIST dataset, the accuracy of the two is 98.61% and 98.04% respectively. Compared with CNN that has similar pipeline architecture, spiking convolutional neural network has a 50% reduction in average energy consumption.