Lung lobes segmentation based on CT images is one of the most important references for doctors to diagnose and treat lung diseases. However, the blurred boundary of lung lobes and the huge workload of manual segmentation make it difficult for doctors to segment lung lobes accurately and quickly. To solve this problem, a new method of automatic segmentation of lung lobes based on 3D full convolution neural network is proposed. Firstly, the original CT image was preprocessed, then the convolution neural network was trained with the preprocessed image. Finally, the image to be segmented was input into the trained network model, and the lung lobe was segmented automatically. The experimental data included CT images of 50 patients with pulmonary diseases from Shanghai Pulmonary Hospital, 30 of which were used for training and 20 for testing. The segmentation results were quantitatively evaluated, with Dice coefficient of 0.961 and Jaccard similarity coefficient of 0.916. Experimental results show that the proposed automatic lung lobes segmentation algorithm has better segmentation performance and stronger generalization ability. It can segment the lung lobes accurately and quickly even when the training set data is small.