CNN-SVM hybrid algorithm combines the feature extraction ability of CNN and the classification performance of SVM, it has certain advantages in computational complexity and can solve small sample problem. It has been applied in fault diagnosis, medical image processing and other fields, at the same time, it gets attention in the field of edge computing due to its low computational complexity. Aiming at the requirements of algorithm performance and power consumption in edge computing scenarios, an optimization and implementation method of CNN-SVM algorithm for FPGA platform is proposed. First, combined with the architecture characteristics of FPGA, the hardware adaptability optimization of CNN-SVM algorithm structure is carried out, including the model compression and the selection of kernel function of classifier. Secondly, the design and implementation of CNN-SVM algorithmic accelerator is completed by using software and hardware cooperation and high level synthesis ( HLS) design method. The experimental results show that on ZCU102, the frames per second(FPS) of accelerator reaches 18. 33 K, the computing speed is 1. 474 GMAC/ s. Compared with the CPU platform, quad core Cortex-A57 and Ryzen7 3700x achieve 23. 57 and 4. 92 times acceleration respectively, compared with Jetson Nano GPU and GTX750 platform, the energy consumption ratio is 33. 24 and 50. 27 respectively.