Determining the lining type is crucial in the design of high-speed railway tunnels constructed using a mining method. This is strongly influenced by geography and geology as well as depends on the designers subjective experience. In this study, an overall framework of tunnel lining-type intelligent design is proposed, and a tunnel lining-type intelligent design system is established. A systematic analysis of factors influencing tunnel lining-type design is performed, collecting design data from 4 100 lining segments across 248 tunnels, and a tunnel lining survey and design database is developed. Three classical machine learning models random forest, support vector machine, and gradient lifting tree are employed, and model hyperparameters are optimized using Bayes and crossvalidation methods. Additionally, three deep neural network models—convolutional neural network (CNN), CNN+long short-term memory, and CNN+efficient pyramid squeeze attention block (CNN+EPSAnet)—are also employed. Prediction results of the test set indicate that: (1) The prediction effect of the CNN+EPSAnet-based tunnel lining-type intelligent design model outperforms others, with a prediction accuracy of 86.8%. CNN+EPSAnet not only fully considers the characteristics of the tunnel lining survey and design data but also effectively extracts multiscale spatial information. (2) A tunnel lining intelligent decision system is developed, and the real-time adjustment of model parameters and visual training of the tunnel lining-type intelligent decision model are realized. (3) The lining-type design parameters obtained via intelligent decision are displayed in three dimensions through building information modeling technology, and the deep integration of intelligent design and digital presentation of tunnel lining types is realized.