In ultra-deep drilling environments, downhole measurement tools often fail or cannot be deployed due to extreme high-temperature and high-pressure (HPHT) conditions. Consequently, mud-logging data become one of the few reliable real-time information sources for evaluating drilling performance and identifying abnormal conditions. This study proposes a data-driven framework for automatic identification of drilling operation statuses using machine learning, with a particular focus on ultra-deep and HPHT wells. A support vector machine (SVM)-based classification workflow was established to recognize nine representative drilling operation statuses from mud-logging data. Through systematic model optimization, the proposed method achieved a classification accuracy of 91.33%. By incorporating a sliding window-based time-series optimization strategy, the overall accuracy was further improved to 95.22%, while the recognition accuracy of HPHT-related operations increased from 77.67% to 89.33%. These results demonstrate that the optimized model possesses strong adaptability and stability under extreme HPHT conditions. This study specifically targets HPHT environments with limited downhole data and incorporates time-series optimization to enhance model robustness. The proposed framework provides a reliable approach with potential for generalization for intelligent operation recognition in ultra-deep drilling, supporting real-time decision-making and improving operational safety and efficiency in challenging environments.