We propose a deep model for pattern classification of computed tomography (CT) images of lung tissues based on the improved deep resiual netwk (ResNet). To address the problem of lack of availability training data, we adopt a transfer learning method to reduce the requiement of a neural network model for large data, thereby decreasing overfitting. The transfer learning strategy uses massively available unlabeled lung CT data as the pre-training data. We perform unsupervised representation learning by maximizing the deep mutual information and matching the prior distribution. The results of contrast experiments show that the improved ResNet achieves improved classification accuracy, the effectiveness of utilizing the unlabeled lung CT data for transfer learning and the classification performance of the network model is improved.