Abstract: Lung cancer is the malignant tumor with the highest mortality rate. Early diagnosis of lung nodules is the key to reducing the mortality of lung cancer. Artificial intelligence technology based on deep learning can continuously improve the accuracy of lung nodule detection and diagnosis through self-learning, which is an important means to achieve computer-aided diagnosis. This article first briefly introduces the concepts of artificial intelligence, machine learning, deep learning, and the relationship between the three. The paper describes four common deep learning models: convolutional neural network, massive-training artificial neural network, auto-encoder, and deep belief network. The convolutional neural network is the most commonly used deep learning model, mainly including two dimensional convolutional neural network, three dimensional convolutional neural network and multi-stream multi-scale convolutional neural network, of which multi-stream multi-scale convolutional neural network it is more conducive to the classification of lung nodules. The massive-training artificial neural network has advantages in limited lung nodule training samples. The auto-encoder can detect lung nodules in a lower dimensional space.The deep belief network is a generation mode. Combining with extreme learning machine could improve the diagnosis rate of pulmonary nodules. Finally, it analyzes the current problems of artificial intelligence: too few labeled images; insufficient interpretability and controllability. There are ethical and legal issues. In short, artificial intelligence based on deep learning has changed not only imaging, but also all other medical fields, and has broad application prospects.