Abstract: The classification of pulmonary nodules is one of the important issues in early detection and diagnosis of lung cancer. To address the problem of information redundancy in multi-scale feature fusion and lack of discriminative feature representation in existing lung nodule classification methods, a multi-scale feature complementation and aggregate constraint pulmonary nodule classification network (MFCAC) is proposed. A multi-scale feature complementation module is proposed to learn the difference information of adjacent scale features, thereby avoiding information redundancy in the feature fusion process. Meanwhile, aggregate constraint loss is introduced into the network feature layer to achieve aggregation of similar features and improve the discriminative feature representation ability of the network. The two modules are integrated into the encoder-decoder architecture to form MFCAC, which can achieve efficient classification. Comparative experiments are conducted on the LIDC-IDRI dataset, and ablation experiments are used to analyze the contributions and effects of each component in this method. The results show that MFCAC has better performance in lung nodule classification compared to the compared algorithms.