Limited by sample number and empirical classification, the conventional rock classification methods are usually applicable for specific types of rock only. Due to lack of elaborate consideration of unfavorable geological conditions, insufficient utilization of multisource geological data, and low intelligentization level, the accuracy and efficiency of rock classification for tunnels under complex geological conditions are limited. To address these problems, with multisource data obtained from geological survey, advanced geological forecast, rock physical experiments, tunnel face survey and other technologies, an intelligent rock classification method for tunnels is proposed in this paper based on multisource heterogeneous data fusion by comprehensively considering the influence of rock properties, discontinuities, geological settings, engineering construction and other factors on rock classification. A hybrid deep neural network model that correlates multisource heterogeneous data with rock classification is built by integrating multiple network modules. In this model, different network structures are selected to carry out feature learning according to different types of input data from different sources, and multiclass target learning is conducted to realize intelligent rock classification with good flexibility.