<p>Accurate geosynchronous satellites status forecasting is essential for improving space situational awareness and supporting downstream tasks such as maneuver detection and intent inference. However, current models face difficulty modeling high-orbit observation data due to its strong non-stationary and weak periodic structure, which hinders effective long-range dependency learning and adaptation to evolving orbital dynamics. To address these challenges, we propose <b>RAPG</b>, a Retrieval-Augmented Patch Generation framework for geosynchronous satellite status analysis, including status forecasting and intent classification tasks. RAPG integrates two synergistic components: (1) an adaptive frequency-domain patching mechanism that dynamically segments satellites’ status time series according to their dominant spectral characteristics, enabling the model to capture both short-term and long-term temporal structures; and (2) a retrieval-augmented generation module that accesses historically similar patches from the training set, providing explicit external memory for reconstructing weakly periodic and non-repetitive motion patterns. By linking adaptive patching with retrieval-based augmentation, RAPG enhances temporal representation learning and generalization across diverse orbital conditions. Experiments on two synthetic datasets and one real-world dataset show that RAPG consistently surpasses strong baselines in both status forecasting and intent classification. RAPG yields the lowest forecasting errors and achieves an F1-score of 0.9461 and an accuracy of 0.9443 for intent classification, outperforming baseline methods. These results confirm that RAPG provides a robust and scalable framework for real-time analysis of geosynchronous satellites, offering significant potential for enhancing remote sensing-based space domain awareness and dynamic orbital intelligence.</p>