To improve the detection rate of pulmonary nodules in early lung cancer screening, a low-dose CT pulmonary nodule detection algorithm based on 3D convolution neural network was presented. First, the multi-directional morphological filtering algorithm was used to preprocess low-dose sequence CT image. The improved 3D region growth algorithm combined with the convex hull algorithm was used for lung parenchymal segmentation. Then the 3D candidate nodules were routed and illuminated in order to solve the convolution neural network on the sample imbalance sensitive issues. Finally, in situations of different network parameters, four groups of experiments were performed on the 50 sequences of low-dose lung cancer screening data in ELCAP database. The results showed that accuracy, sensitivity, specificity and ROC curve of the AUC values were 84.6%, 88.89%, 80.32% and 0.924 4 respectively by the constant optimization of network parameters. The proposed algorithm can correctly detect low-dose lung nodules, with the the accuracy, sensitivity, and specificity increased by 5.37%, 5.6% and 10.42%, respectively, which is more comprehensive and can provide effective help for lung cancer screening compared with conventional lung nodule detection algorithm.