Diseases detection and maintenance of tunnel lining is an important link to ensure the safety of tunnel in operation. Based on the images captured by CCD linear array camera in Movable Tunnel Inspection System, a new method was proposed. It is inspired by cutting-edge computer science-deep learning and different from the traditional ones entirely. The main idea is as follows: a) extracting lining diseases and establishing feature map database; b) building deep learning framework; c) training samples with convolutional neural network; and d) establishing a classification system of gray scale feature maps of tunnel lining. Aiming at CNN model GoogLeNet, inception module and overall architecture were improved by using improved convolutional kernels. The best test-set accuracy is over 95%. At the same time, the influence of different deep learning frameworks (Caffe and Torch) and image contrast enhancement method (such as histogram equalization, HE) were tested with examples. The results show that the deep learning method is applicable to the tunnel lining diseases detection. The advantages are high accuracy, high speed, good extensibility and very robust in complex cases.