Geological mapping is a basic work for geology. However, the working areas for geological mapping are mostly mountainous areas with high elevation and steep terrain which is difficult for field work. It’s urgent to develop a semi- automatic to automatic lithologic mapping method using remote sensing data by combining the spectral features and spatial features of each lithologic unit. Therefore, this study, taking Baixiani Mountain, Beishan Mountains, Gansu Province, as the working area, utilized two machine learning methods to test the feasibility for automatically lithologic mapping. Methods:Two methods, support vector machine and extreme learning machine, combined with a spatial feature extraction method, quick shift algorithm, were used to process ASTER remote sensing data for lithologic classification. Results: The overall accuracy of support vector machine classification was 89.17%, while the extreme learning machine not only had the advantage of requiring fewer adjustable parameters, but also had higher classification accuracy and speed than the support vector machine, with an overall accuracy of 96.70%. The use of image spatial features extracted by the quick shift algorithm effectively reduced misclassification areas and improved lithological classification.Conclusions: The study confirmed that the lithological classification method combining extreme learning machine based on spectral features and quick shift algorithm based on spatial features has advantages such as objectivity, efficiency, and high accuracy, and can provide reliable data support for subsequent geological mapping and mineral exploration work, with high promotion value in the field of remote sensing lithological classification.