This study designed a one-stop platform for automatically extracting patches from multisource domestic satellite images based on a deep learning framework. The platform focuses primarily on critical techniques including semantic segmentation of ground objects, swarm intelligence algorithms for patch extraction, and deep feature interpretation. To address challenges in remote sensing image interpretation, such as significant color differences, vast data volumes of single images, diverse multi-channel image representations, and considerable differences in the sizes of remote sensing targets, the platform incorporates intelligent semantic segmentation and swarm intelligence algorithms for automatic patch extraction into the framework. It offers a range of customizable general and specialized models while supporting the self-training of models. With functions including large-scale data management, data annotation, model training, model testing, patch extraction, and application analysis, the platform has been successfully applied to the intelligent semantic segmentation and patch extraction of ground objects like buildings, vegetation, farmland, industrial zones, and water bodies in Taiyuan City, Shanxi Province based on multisource domestic satellite images.