Rapid, accurate and exhaustive research on the distribution of mudslide-hostile areas is of great significance, enabling us to understand and have a deep understanding of the scope of distribution of mudslides, the distribution pattern, the causes of the mudslides, and to further find scientific monitoring, prediction, prevention and management of the technical means by the specific situation, to reduce the problems and losses brought about by the mudslide disaster. To seek an efficient and high-precision method for extracting the spatial distribution of mudslides, this study chooses the Xiaojiang River Basin in Yunnan Province as the study area, employed the random forest algorithm based on the Google Earth Engine (GEE) platform to extract the spatial distribution of debris flow traces efficiently. Firstly, Four types of feature variables(spectral features, index features, topographic features, and texture features)were constructed using the 2022 Sentinel-2 image and topographic data, then the random forest feature variable importance score and the J-M distance were combined for the feature preference research and analysis, explored the importance of each feature variable on the extraction of mudslide traces, and finally, set up various feature combinations to create six schemes, compared and analyzed the accuracy of the debris flow traces extracted by the six experimental schemes, and found the best scheme to increase the recognition accuracy. The study shows that: (1) regardless of feature optimization, the accuracy of debris flow trace identification with the addition of terrain feature variables is higher than that with merely optical image data, indicating the utility of using terrain data for debris flow trace information extraction; (2) classification accuracy is affected differently by different feature variable kinds; topographic, index, texture, and spectral features are the feature types with the highest to lowest feature importance scores; (3)the experimental scheme 6 is the best results of the spatial distribution map of debris flow traces in Xiaojiang River Basin, Yunnan Province, in 2022, which constructed multi-dimensional feature variables and feature optimization based on the multi-source data of Sentinel-2 optical images and topographic data. This resulted in an overall accuracy of 94.95%, a Kappa coefficient of 0.94, a debris flow trace mapping accuracy of 91.01%, and a user accuracy of 95.29%. Furthermore, the scheme effectively reduced data redundancy while improving the classification accuracy. This study makes use of the Google Earth Engine (GEE) platform. These multi-source data combine topographic and optical remote sensing imagery and the Random Forest algorithm, which can quickly, accurately, and efficiently extract information on debris flow traces in areas with complex feature coverage over a large range of terrain and has a large potential for applications.