Abstract: Cervical cancer is a malignant tumor that highly endangers women’s lives. Cytological screening based on image processing is the most widely used detection method for precancerous screening. Recently, with the development of machine learning theory based on deep learning, the convolutional neural network has made a revolutionary breakthrough in the field of image recognition due to its strong and effective extraction ability. In addition, it has been widely used in the field of medical image analysis such as cervical abnormal cell detection. However, due to the characteristic high resolution and large size of pathological cell images, most of its local areas do not contain cell clusters. Moreover, when the deep learning model uses the method of exhausting candidate boxes to locate and identify abnormal cells, most of the sub-images obtained do not contain cell clusters. When the number of sub-images increases gradually, a large number of images without cell clusters as input to the object detection network will make the image analysis process redundant for a long time, which drastically slows down the speed of detection of the large-scale pathological image analysis. In view of this, this paper proposed a new detection strategy for abnormal cells in cervical cancer microscopic imaging. According to the pathological cell images obtained by the membrane method, the image classification network based on deep learning was first used to determine whether there were abnormal cells in the local area. If there were abnormal cells in the local area, the single-stage object detection method was used for further pathological cell image analysis, so that the abnormal cells in the images could be quickly and accurately located and identified. Experimental results show that the proposed method can double the speed of detection of cervical cancer abnormal cells.