Lung nodule classification is an important task in the diagnosis of early-stage lung cancer. Although the lung nodule classification methods based on deep learning can achieve good classification accuracy， they have the problems of complex model and poor interpretability. Therefore， a lung nodule classification algorithm based on neural network architecture search was proposed. Firstly， the attention residual convolution cell was regarded as the basic unit of the search space， and the partial order pruning method was used as the search strategy to construct the neural network architecture for searching 3D classification network， thereby achieving the balance between network performance and search speed. Then， the multi-scale channels and spatial attention modules were constructed in the network to improve the interpretability of feature description and categorical inference. Finally， the stacking method was used to merge the searched network architectures with multiple models to obtain accurate prediction results of classification of benign and malignant lung nodules. Compared with the state-of-the-art lung nodule classification methods， the proposed algorithm has better classification performance and faster convergence on the widely-used lung nodule classification dataset LIDC-IDRI. Moreover， the proposed algorithm has the specificity and precision reached 95.37% and 93.42% respectively， showing it can achieve accurate classification of benign and malignant lung nodules.
