Cytoendoscopy requires continuous amplification with a maximum magnification rate of about 500 times. Due to optical fiber illumination and stray light, the image has non-uniform illumination that changes with the magnification rate, which affects the observation and judgement of lesions by doctors. Therefore, we propose an image non-uniform illumination correction algorithm based on the illumination model of cytoendoscopy. According to the principle that image information is composed of illumination and reflection components, the algorithm obtains the illumination component of the image through a convolutional neural network, and realizes non-uniform illumination correction based on the two-dimensional Gamma function. Experiments show that the average gradient of the illumination channel and the discrete entropy of the image are 0.22 and 7.89, respectively, after the non-uniform illumination correction by the proposed method, which is superior to the traditional methods such as adaptive histogram equalization, homophobic filtering, single-scale Retinex and the WSI-FCN algorithm based on deep learning.