The invention belongs to the technical field of heterogeneous calculation and image identification, and particularly relates to a convolutional neural network algorithm design implementation method based on heterogeneous calculation. According to the method, an implemented hardware platform is a Xilinx ZYNQ-7020 programmable SoC (system on a chip), an FPGA (field programmable gate array) and an ARM (advanced RISC machine) processor are arranged in the hardware platform, an implemented software platform is an SDSoC, and high-level synthesis and software definition connecting frame are combinedtogether, so that a HLS (high-level synthesis) result can be seamlessly connected to a software application. According to the method, a network model and a training network model are designed on a PC(personal computer), a network model parameter is extracted on the PC, software and hardware code partition is rapidly performed on a convolutional neural network algorithm on the SDSoC, inputted dataimage preprocessing, a pooling layer and a classification algorithm are implemented on an ARM terminal, convolution operation with maximum calculated amount is mapped to the FPGA and implemented, andperformance and area required by a system are met. According to the method, a convolutional neural network algorithm is rapidly implemented by the aid of a heterogeneous platform, the efficiency of the algorithm is greatly improved, and power consumption is greatly reduced when accuracy of the convolutional algorithm is ensured.