Abstract： Aiming at the intelligent diagnosis of emphysema highly depending on high-quality annotation data,complex image spa-tial information and insufficient feature extraction,we designed an emphysema classification algorithm based on Goddard scoring meth-od.Firstly,the algorithm utilized the SimSiam framework for self-supervised learning to address the dependency on a large volume of high-quality annotated data.Then,the continuous 3D convolution module and the efficient multi-scale attention(EMA)module were introduced,to capture the key spatial information of lung images by integrating the information of upper,middle and lower lung lobes,to improve the feature extraction ability and recognition accuracy of the model were processing complex lung CT images.The experimen-tal results showed that in the grading task of the emphysema presence,mild and no emphysema,and the severity of emphysema,the accuracy of the model was 88.79%,83.44%,and 57.4%,respectively.The result indicates that this algorithm performs well in the em-physema recognition and classification,and has certain clinical significance.