Objective: To explore the capability and clinical application potential of the Faster Region-based Convolutional Neural Network (Faster R-CNN), an Artificial intelligence algorithm, in identifying the composition of urinary calculi from CT images. Method: This was a retrospective study. Data from 776 patients with urinary calculi treated at the Affiliated Hospital of Hebei University from August 2020 to December 2023 were collected. Patients with simple calculi were randomly divided into a model construction group and validation Group-I at a 5:1 ratio, while 60 cases of mixed calculi were randomly selected to form validation Group-II. The model construction group was employed to construct and test the performance of the Faster R-CNN model, while the validation groups were used to verify the model's performance. Results: In validation Group-I, the model achieved an area under the curve (AUC) of 0.843. In validation Group-II, the kappa values for the model's prediction of calcium oxalate and uric acid components, consistent with infrared spectroscopy analysis, were 0.649 and 0.653, respectively. Conclusion: Faster R-CNN demonstrates a robust capability for quantitative prediction of the composition of urinary calculi, indicating substantial promise for clinical applications.