In this study, the convolutional neural network under the deep learning framework is applied to the field of cervical cell identification to achieve automatic classification of cervical cell images. Firstly, the cervical cells are pretreated, and the problem of different image input sizes is solved by nuclear cutting, the image is flipped and translated, the data set is expanded, and the sample size imbalance is solved. Then the VGG-16 network is selected for improvement. The improved VGG-16 network is used for feature extraction and cell classification. The migration learning method is used for network pre-training, which speeds up the network convergence speed and improves the classification accuracy. Finally, through the training of the network, it achieves better result. According to the classification results, the classification accuracy is improved compared with the manual extraction feature design classifier. The accuracy of two categories classification is 97.3%, and the accuracy of the seven categories classification is 89%. The experimental results show that the convolutional neural network automatically classifies the cervical cell images, and the classification accuracy is better than that of the artificial extraction feature classifier, and the classification results are not affected by the segmentation image accuracy.