A three-dimensional (3D) deformable convolutional neural network is proposed based on the C-3D convolutional neural network for realizing detection of pulmonary nodules. A 3D deformable convolution and pooling is used in the main structure of the model. It solves the problem that the traditional square convolution and pooling cannot collect the pixels of pulmonary nodules efficiently when dealing with irregular pulmonary nodules. By adjusting the input of the 3D convolutional neural network, the scanning and recognition of 32×32×32 pixels of a sample image are realized step by step by using a convolutional neural network, thereby realizing pulmonary nodule localization. As for the output of the model, the first full connection layer of the C-3D network is replaced by the convolution layer based on a full convolution neural network, to solve the problem of memory overflow during training. In terms of model parameters, three different learning rates and optimization functions are designed for experimental comparison, and the parametric comparison diagrams of three different learning rates and optimization functions are drawn. According to the experimental results, the optimal learning rate and parameters of optimization functions of the convolutional neural network are selected. The experimental results show that the area under the receiver operating curve, classification accuracy, recall, and F1 value of the proposed method have been significantly improved.