Gun-related cases pose extreme societal harm and urgently demand efficient and precise detection methods. This study aims to integrate the reflectance transformation imaging (RTI) technique with deep learning to apply it to the field of gun and bullet recognition. In the experiment, a total of 1500 samples of fired cartridge cases were selected from five QSZ92 9 mm pistols. Detailed images of the markings on the base of the cartridge cases were captured using the DTV3.1 intelligent imaging system to obtain their normal maps. Thereafter, the pre-trained ResNet-50 network extracted features from the normal maps and underwent classification training. The model’s performance was evaluated by outputting AUC values, accuracy on the test set, and a confusion matrix. The experimental results reveal a total AUC value of 0.98 across the five guns, with gun No. 2 achieving the highest accuracy of 97.66% and gun No. 1 the lowest at 93.75%. This study demonstrates that the automatic recognition method of cartridge case marks based on RTI technology and deep learning yields significant results, offering valuable reference for the identification of other traces in the field of trace inspection.