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In order to improve the recognition efficiency of lung diseases and reduce the rate of missed diagnosis of lung nodules, a set of intelligent detection and three-dimensional visualization system of lung nodules was designed. Methods: A deep multi-channel three-dimensional convolutional neural network based on RESNET was constructed. Based on the 888 patient images of the LUNA16 public data set, a Focal loss loss function with α = 0.5 and γ = 2 was selected for training. The suspicious lung nodules are detected, and the ray projection algorithm is used to perform volume rendering three-dimensional reconstruction of the detected nodules. Results: After experimental tests, the network has the highest accuracy compared with the single-channel network and Feature Pyramid network (FPN), which is 84.8%. The system can automatically detect lung nodules and complete 3D reconstruction within 230s. The sensitivity of CT images with a resolution of 1mm / pixel is above 98%. Users can view the nodule detection results and 3D reconstruction models on the browser. Conclusion: The system breaks through the limitation of terminal equipment and area, and can provide auxiliary diagnosis for lung diseases and improve the diagnosis efficiency.