Intelligent identification of low-resistivity thin oil layers is crucial for improving logging interpretation accuracy in complex reservoirs. In Dongying formation of Chengbei 35 block, Chengdao oilfield, the low-resistivity and thin interbed characteristics lead to ambiguous logging responses and minimal differences between productive and non-productive layers. This paper innovatively applies the Gradient Boosting Decision Tree (GBDT) model for intelligent identification of lowresistivity thin oil layers. By integrating logging curve characteristics, lithoelectric test results, production data, and reservoir physical properties, a logging feature set of low-resistivity layers is constructed through mathematical feature extraction. Key discrimination parameters are selected via a decision tree feature selection mechanism as input for the GBDT model, establishing an intelligent identification model for low-resistivity reservoirs. Combined with lithoelectric test data, the reservoir lower limit standards are determined. Application results show that the GBDT model achieves an identification accuracy of 89.5%, approximately 30% higher than the traditional logging numerical model, significantly reducing errors caused by manual interpretation and providing an intelligent solution for efficient exploration and development of low-resistivity thin oil layers.