Herein, a method of pulmonary nodule recognition based on a three-dimensional (3D) convolution neural network (CNN) is proposed to overcome the problem of false positives in pulmonary nodule detection by traditional computer aided detection systems. First, a traditional two-dimensional CNN is extended to 3D CNN to fully extract the 3D features of pulmonary nodules and enhance the expressive ability of the features. Second, dense connection network and SENet are combined to enhance feature transfer and reuse, and feature weights are adaptively learned by feature recalibration. In addition, focal loss is introduced as the network classification loss to improve the learning of hard examples. The experimental results on the LUNA16 dataset demonstrate that the proposed network model achieves sensitivities of 0.911 and 0.934 at one and four false positives per scan, respectively, and the competition performance metric is up to 0.891, which is better than that of existing mainstream methods.