To address the problem that traditional flight operation performance evaluation methods are highly subjective， analyze parameters one-sidedly， and cannot be quantified comprehensively and objectively， a Multi-dimensional Feature Analysis based Gradient Boosting Regression Tree （MFA-GBRT） method was proposed. In the method， time-domain and trend features of Quick Access Recorder （QAR） data were extracted， and an improved Gradient Boosting Regression Tree （GBRT） was combined with a threshold cumulative importance filtering mechanism， an evaluation index system covering “attitude control-power management-environmental response” and a performance level evaluation model were constructed. Experimental results on simulator and flight base data show that the average evaluation accuracy of the proposed method reaches 87.83%， which is 10.78%， 6.06% and 3.55% higher than those of the existing methods Long Short-Term Memory-Deep Neural Network （LSTM-DNN）， curve similarity method and wavelet analysis， respectively. Cross-scenario validation show that the method has adaptability over 95% （high adaptability） in three different flight scenarios. It can be seen that the method realizes full-process objective quantitative evaluation of the flight process and provides a scientific scheme with engineering practicability for flight operation performance evaluation.