Single pixel imaging is the key technology to solve the problem of imaging in non-visible wavelengths because it only needs a single point detector without spatial resolution to obtain the spatial information of the target object and reconstruct the image of the object. The development and application of this technology in high resolution space have the problems of large data collection and slow imaging. Therefore, this paper designs a Fourier single-pixel microscopy super resolution imaging system to improve the efficiency and quality of existing single-pixel microscopy imaging. Firstly, a single-pixel microscopic super resolution imaging system was built, and a deep learning-based super resolution model was introduced to improve its imaging resolution, so as to quickly obtain high-resolution object reconstruction images. Experimental results show that under the same light intensity signal acquisition time, the imaging resolution can be improved nearly 9 times, and its peak signal-to-noise ratio reaches 28 dB, which effectively improves the imaging efficiency of single pixel microscopic imaging under the condition of high resolution.