Research on Performance Optimization Method of Kylin System Based on Machine Learning

Due to the relatively short development history of Kylin OS, system tuning is required to achieve optimal performance, enhanced maintainability, improved ecosystem compatibility, and reduced operational costs when deployed in a production environment. In order to further solve the performance challenges of Kylin OS in complex business scenarios, this paper proposes a four-phase optimization method integrating multi-dimensional system parameters and integrated learning. By constructing a multidimensional performance monitoring system based on Psutil, we realize millisecond real-time data collection; designing an attention mechanism-enhanced stacking model (XGBoost-LightGBM-CatBoost) to accurately identify complex load states (accuracy:92.3%); innovating a polarity-weighted isolation forest algorithm to improve anomaly detection (accuracy:87.4%); establishing a Bayesian optimization-driven parameter dynamic adjustment mechanism to improve performance in complex business scenes and reduce the response time by 52.7% (from 225ms to 107ms) in Web server scenarios. Experiments show that this method reduces the false alarm rate by 64% compared with the traditional threshold strategy, providing an effective solution for intelligent operation and maintenance of domestic operating systems.