Due to the rapid development in artificial intelligence, the classic Von Neumann architecture is no longer able to meet the demands of high-computing, high-storage, and high-bandwidth artificial intelligence applications. To address this issue, this paper proposes a handwritten digit recognition accelerator based on a systolic array. First, a 5 convolutional layer handwritten digit recognition network is built. Besides, The FPGA-based convolution accelerator employs im2col technology to convert convolution calculations into matrix multiplications and uses a systolic array to efficiently perform these multiplications. Furthermore, the four convolution processes are pipelined to improve the throughput. Finally, a handwritten digit recognition system is implemented on the PYNQ-Z2. Compared with the software implementation on Arm, the speedup of the accelerator is 598x, the delay is 1.24 s, and the power consumption is 2.51 w.