Aiming at the problem that the key imaging sign information of pulmonary nodules is difficult to obtain and the recognition rate of some convolutional neural networks (CNN) models for pulmonary nodules is low, an dynamic convolutional residual networks incorporating attention features (DcANet) is proposed. This network can not only realize the classification of benign and malignant pulmonary nodules, but also visually analyzes the diagnosis results of the proposed model. This network is based on a residual network that adapts to the three-dimensional small-size input characteristics of lung nodules. In the DcABlock part, the dynamic convolution and iterative attention feature fusion module are used, so that the model can accurately obtain the information of pulmonary nodules, thereby improving the representation ability. In addition, class activation mapping is used to visually analyze the slices of each layer of the 3D image. The accuracy of this experiment on test set is 85.87%, balanced F score (F1) value is 82.67%, comprehensive index of sensitivity and specificity Gmean value is 85.51%. The experimental results show that the DcANet has satisfactory accuracy in classifying benign and malignant pulmonary nodules, and the diagnosis results are reliable, which has certain clinical application value.