Abstract: Accurate identification of ore-forming intrusions is critical for porphyry copper exploration. Previous studies have extensively used trace-element compositions of magmatic accessory minerals, such as zircon and apatite, to evaluate the metallogenic potential of magmas. However, the trace-element compositions of accessory minerals are controlled by multiple factors, which may limit their effectiveness in recording ore-forming magma signatures. Biotite is a common rock-forming mineral in intermediate to felsic igneous rocks, and its major-element compositions are mainly controlled by the parental magma, making it a potential indicator mineral for magmatic metallogenic potential. Based on a systematic compilation of biotite data from porphyry copper ore-forming intrusions and barren intermediate to felsic intrusions worldwide, this study selected nine major elements of biotite to construct three machine-learning models, namely support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB), for identifying porphyry copper ore-forming intrusions. The results show that all three models perform well, with accuracies of 0.9561, 0.9614, and 0.9587, respectively, indicating that this approach can effectively identify ore-forming intrusions related to porphyry copper mineralization. SHAP analysis further reveals that FeO^T, Na₂O, and MnO are the three most important features controlling model outputs, and that biotite from ore-forming intrusions is generally characterized by low FeO^T, low MnO, and high Na₂O. These results demonstrate that the major-element compositions of biotite can effectively indicate the metallogenic potential of intrusions, providing a new approach for identifying ore-forming intrusions in porphyry copper systems and for guiding exploration of similar deposit types.