Aircraft flight quality assessment is a critical process for evaluating the training effects and improving the training standards of pilots. Traditional evaluation methods rely heavily on the subjective scoring by flight instructors, which suffer from subjectivity and limited accuracy. To enhance the objectivity and precision of flight quality assessment, this paper introduces a novel evaluation method that integrates Grey Correlation Analysis (GCA) with the XGBoost algorithm. GCA is utilized to identify flight parameters closely related to flight quality, while the XGBoost algorithm is employed to construct a flight quality assessment model. The high accuracy of the proposed method is verified through the evaluation of actual flight training data. The study demonstrates that the method can effectively enhance the scientific and precise nature of flight quality assessment, providing robust technical support for pilot training.