To capture the three-dimensional irregularities of nodules in lung CT images and improve their diagnostic accuracy， a double-dimensional convolutional neural network （d3D-CNN） nodule diagnosis model from sieve to diagnosis is designed. Firstly， a lightweight 3D-CNN network is constructed， it is combined with full convolution operation， and the high optimization of convolution operation is used to complete nodule screening and generate suspected regions. Then， the space-slice attention mechanism is used to automatically learn the offset of the suspected region in space and slice sequence， design a deformable 3D convolution module， and combine it with ResNet101 to construct a high-precision 3D-CNN nodule diagnosis network for the final judgment of the screened suspected region. The comparative experimental results show that the recall rate of the proposed model reaches 88.9% under the false alarm rate of 1， which effectively improves the accuracy of benign and malignant diagnoses of pulmonary nodules.