Abstract: There are many fluorescence detection methods currently used for logging, but they all have problems such as single activation light source, limited quantitative evaluation accuracy, and complex detection calculation methods. A fluorescence intelligent detection system based on artificial intelligence for logging rock cuttings has been designed to address this issues. The system can adjust the wavelength of the light source according to the type and area of the cuttings and uses an industrial camera to capture high-definition images that are conducive to detection by deep learning algorithms. It employs an improved DeepLab v3+ algorithm embedded in a mobile device to detect cuttings fluorescence, calculate the fluorescence ratio, and display the results and detection images on the mobile device screen. Test experiments conducted on a large number of various cuttings samples show that the system achieves an accuracy rate of 72.73% in detecting cuttings fluorescence, ensuring both accuracy and timeliness while quantifying fluorescent areas in the rock samples. The intelligent monitoring system for cuttings fluorescence based on the improved DeepLab v3+ algorithm resolves uncertainties present in manual fluorescence detection processes and meets the practical needs of fluorescence logging technology for cuttings fluorescence detection.