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In view of the fact that shallow convolutional neural networks are difficult to obtain the deep features of the image and are easy to overfit, which leads to low classification efficiency and accuracy, a deep learning recognition model for lung tumor images is designed. Based on the use of data augmentation and transfer learning, and the improvement and improvement of the AlexNet convolutional neural network, the data is subjected to a normal preprocessing before the data input of each layer of the network, while applying a linear rectification function (ReLU). Realize fast acquisition of lung tumor expression characteristics, and the output end is classified through three fully connected layers and softmax algorithm.The experimental outcome indicate that the proposed method achieves better performance in terms of network convergence speed and classification accuracy，which is 5.66% higher than that based on the AlexNet convolutional neural network,and it has good robustness．