Low efficiency of Quick Access Recorder （QAR） data analysis highlights the importance of feature extraction from QAR data. In response to issue of insufficient focus on temporal trend features in QAR data feature extraction， Piecewise Cubic Hermite Interpolating Polynomial （PCHIP） module and ordering relation algorithm （G1） weight assignment module were integrated to form the interpolation weighting， Piecewise Cubic Hermite Interpolating Polynomial — ordering relation algorithm （PG）， then a Convolutional AutoEncoder （CAE） was combined to construct PG-CAE model， and a flight pitch operation feature extraction method based on QAR data was proposed to support analyses such as flight-level anomaly detection. Firstly， the PCHIP was used to standardize data length. Secondly， the G1 weight assignment module was used to determine the weights according to causal temporal correlation between flight operations and flight attitudes， thereby quantifying temporal importance of flight pitch operation data. Thirdly， the CAE module was employed to extract features from the weighted data. Finally， the model validation was performed on the basis of pitch operation data from 406 flight segments of a certain airline's A319 aircraft. Experimental results indicate that PG-CAE model outperforms CAE model significantly by introducing PCHIP and G1 modules， so as to employ the reconstruction error to measure compliance of individual data samples with original data， which determines model acceptability， and use standard deviation to assess model's capability to extract overall trend features from dataset. Ultimately， CAE5 model with a 5- convolutional-pooling layers is identified as the optimal model structure， demonstrating a reconstruction error of 0.032 84 and standard deviations of （0.162 1， 0.280 5）. Furthermore， by combining K-means algorithm， a comparison of point clustering effect after PG-CAE feature extraction with curve clustering effect without feature extraction further demonstrates that PG-CAE model can extract line cluster data of temporal trend data as point cluster data of two-dimensional features， serving research such as flight-level anomaly detection based on QAR data.