To address the challenges of low efficiency and high computational costs faced by deep learning models in tunnel surrounding rock integrity analysis, a dual-model strategy based on YOLO and DeepLab is proposed, enabling rapid assessment of tunnel surrounding rock integrity. This method constructs a rock fracture recognition model based on the YOLO algorithm and introduces the DeepLab image semantic segmentation algorithm to intelligently extract fracture parameters, with results visualized. By integrating the processed image information, a new indicator for evaluating the development degree of rock fractures—the Fracture Factor (F_f)—is introduced to quantitatively analyze the development degree of surrounding rock fractures and determine the surrounding rock integrity coefficient (K_v). Using 5 000 images from typical domestic engineering projects for testing, the results show that the dual-model strategy based on YOLO and DeepLab achieves a fracture recognition accuracy of 96.41% and a fracture segmentation accuracy of 94.48%. Compared to traditional deep learning algorithms, the Fracture Factor (F_f) provides an assessment of surrounding rock integrity that is closer to the integrity coefficient in the BQ method. The dual-model strategy significantly reduces computational costs while ensuring accuracy, enhancing the feasibility of deep learning models in practical engineering applications.