Convolution neural network has made outstanding achievements in the field of image processing.However,the huge amount of computation in the algorithm leads to higher power consumption and poor real time performance,which limits the practical application of neural networks.If the neural network is transplanted to the FPGA hardware platform,it can give full play to its highly parallel advantages to achieve network acceleration,reduce power consumption,and improve the real time performance of the algorithm.In this paper,the network model for target classification is successfully translated to FPGA.By comparing the alarm results before and after adding the classification model,which shows the importance of the designed model.By comparing the hardware implementation with the simulation results,the correctness of the hardware implementation is proved.