To address the complex spatiotemporal characteristics and temporal domain fluctuation challenges in flight trajectory prediction, this study proposes a method integrating spatiotemporal dual extraction and frequency-domain enhancement. The proposed method combines the temporal convolutional network (TCN) with the iTransformer model to capture local temporal features and global variable interactions in trajectory sequences. This enables dual extraction of data features at different levels and granularities, effectively uncovering potential spatiotemporal correlations. The frequency enhanced channel attention mechanism (FECAM) is introduced, which converts trajectory features into the frequency domain using the discrete cosine transform and strengthens the frequency-domain information with channel attention, reducing the impact of temporal domain fluctuations. Experiments on a 3D flight trajectory dataset show that during climb, cruise, and descent phases, the proposed method achieves mean absolute error of 1.15, 0.15, and 0.82, respectively, demonstrating significant advantages in prediction accuracy and stability over existing methods.