Abstract: In this study, an image processing and deep learning method for identifying different types of rock images was proposed. Preprocessing, such as rock image acquisition, gray scaling, Gaussian blurring, and feature dimensionality reduction, was conducted to extract useful feature information and recognize and classify rock images using TensorFlow-based convolutional neural network (CNN) and PyQt5. A rock image dataset was established and separated into workouts, confirmation sets, and test sets. The framework was subsequently compiled and trained. The categorization approach was evaluated using image data from the validation and test datasets, and key metrics, such as accuracy, precision, and recall, were analyzed. Finally, the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image. The experimental results indicated that the method combining deep learning, TensorFlow-based CNN, and PyQt5 to recognize and classify rock images has an accuracy rate of up to 98.8%, and can be successfully utilized for rock image recognition. The system can be extended to geological exploration, mine engineering, and other rock and mineral resource development to more efficiently and accurately recognize rock samples. Moreover, it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme. The system can serve as a reference for supporting the design of other mining and underground space projects.