Deep learning technology is widely used in target detection tasks because of its powerful feature extraction capabilities.Aiming at the problems of uneven recognition accuracy and low detection efficiency of multi-scale cervical cancer cells,this paper proposes an improved recognition algorithm,mini-object-YOLO v3 (mo-YOLO v3) based on the YOLO v3 model.The cervical cell images collected under a 20× digital scanner are selected as the data set.In order to improve the robustness of the algorithm,multiple data enhancement strategies such as contrast enhancement,grayscale image,rotation and flipping are introduced to expand the data set;the model takes Darknet53 network combined with attention mechanism as the backbone module,for the large difference in the size of cervical cancer cells,a multi-scale feature fusion algorithm is proposed to optimize the model structure.In order to solve the problem of low detection accuracy of small targets,an improved loss function is proposed,adopting the relative position information method to reduce the influence of the object frame on the detection result.The test results show that the mo-YOLO v3 model proposed in this paper not only has obvious advantages in overall recognition accuracy,but also greatly improves the positioning accuracy of small-size cervical cancer cells.The model has an accuracy rate of 90.42% for identification of cervical cancer cells,a precision rate of 96.20%,a recall rate of 93.77%,and a similarity index ZSI of 94.97%,which is higher than similar algorithms.