Abstract: The geochemical composition of basalt is closely related to the tectonic setting of the formation, thus basalt is an important window for viewing the deep Earth and the composition and geodynamic processes. To discriminate the tectonic setting of basalt formation, although a series of tectonic discrimination diagrams have been established based on the geochemical characteristics of basalt, those discrimination diagrams are limited to two-dimensional or three-dimensional data. With the explosive growth of global geochemical data of basalt, these discrimination diagrams show gradually the shortcomings of being local and inaccurate. Therefore, using machine learning methods is beneficial to analyze data multi-dimensionally and comprehensively, and to establish accurate and efficient discriminant models. A global modern oceanic basalt dataset was established by using GEOROC and PetDB databases through a series of steps from data downloading, training, and analyzing. The dataset was trained by the support vector machine (SVM) and random forest (RF) machine learning algorithms and a high-accuracy and high-dimensional discrimination model was built. In addition, the accuracies of different machine-learning algorithms training were analyzed against different geochemical composition datasets of modern oceanic basalt, and the discrimination models were applied to ophiolitic basalt to explore the application of machine learning models for ancient oceanic basalt. This work provided a higher-dimensional approach to discriminate oceanic basalt, and a successful attempt of using machine learning in earth science in the era of the big data.