Many large-scale energy, transportation, water conservancy, and hydropower engineering projects take place in a rock mass environment. It is still difficult to accurately extract and describe rock mass structural information, which is crucial to analyzing rock mass deformation and stability. As a major rock mass surface exposure type, the structural surface has an irregular and rough shape, for which it is not appropriate to use traditional image processing methods. An intelligent extraction method is proposed for rock structural surfaces based on the integration of multimodal semantic features and a full convolutional neural network. The main contents of the proposed method are as follows: (1) generation of a dense point cloud model of the rock mass surface using multiview images and 3D geological semantic feature expression; (2) establishment of the mapping relationship among multimodal semantic features; (3) homogeneous unit extraction through integration of a full convolution neural network model; and (4) homogeneous unit merging and clustering. The method’s feasibility is proven through experiments, and its completeness and accuracy are verified by comparing results with traditional field measurements. Overall, the method provides a concept for intelligent extraction of rock mass structure information and has important theoretical value and practical significance.