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The conversion of Mongolian fonts plays a pivotal role in promoting the application and dissemination of the Mongolian script, enriching the diversity of Chinese culture, and fostering economic prosperity in Mongolian regions. The method based on lightweight convolutional neural network and FPGA hardware accelerator is adopted to improve the efficiency and accuracy of Mongolian font conversion. By utilizing CNN for handwritten Mongolian script recognition and integrating it with a font conversion library, a simple and efficient Mongolian font conversion is achieved through the mapping relationship between recognition results and fonts. In comparison to other methods, this approach combines the efficiency of CNN and FPGA hardware accelerators, improving conversion efficiency while meeting requirements for device cost, power consumption, and portability. The design and optimization of the network model circuit were accomplished using Xilinx's XC7Z020CLG400-2. On this basis, a handwritten Mongolian font conversion system was implemented. Test results indicate an accuracy of 95.62% for converting handwritten Mongolian script to the target font, a conversion time of 1.43ms, a power consumption of 0.341W, and a peak throughput of 6.64Gops for the accelerator. The research findings hold significant importance for promoting the preservation of Mongolian culture and facilitating economic development among the Mongolian population.