Aiming at the problem that the classification effect of lung nodules in CT images is biased due to blurred edges and unobvious features,this paper proposes a mul ti-model fusion method (MSMA-Net) embedded in the attention mechanism.This method first performs lung parenchymal segmentation and cropping operations on the original C T image to obtain two images of different sizes,and then input them to the spatial atte ntion model and the channel attention model for training.The spatial attention model focuses on extracting lung nodules.The spatial position information of the nodules in CT images,the channel attention model focuses on extracting the detailed features of lung nodules.Fin ally,the features extracted by the two models are fused to obtain the benign and malignan t classification results.A large number of experiments have shown that this multi -model fusion method can well extract the position information of lung nodules in CT images an d their own edge features.Based on the LIDC data set,this method is accurate and sensitive .The specificity reached 96.28%,96.72%,and 96.17%,respectively,which achieved bet ter classification results than traditional network models.