Abstract: Aiming at the problems of low segmentation accuracy and time-consuming feature extraction intraditional classification methods, a convolutional neural network（CNN） for identifying benign and malignantnodules in lung CT images is constructed. Firstly, the network depth, the number and size of convolutionkernel were determined, and the initial model of CNN was constructed. Secondly, selected the activationfunction, learning rate, learning rate decay strategy and other training parameters. Finally, the region of interestwas divided into a large number of local sub regions, and the enhanced data samples were used fortraining. On the LIDC-IDRI dataset, the accuracy, specificity, sensitivity and AUC value were 92.50%, 0.91,0.94 and 0.93 respectively. The recognition ability of malignant nodules is superior to other models.