<p>Accurate Air Traffic Flow Prediction (ATFP) is critical for enhancing airspace safety and efficiency. Achieving high-precision ATFP requires simultaneous consideration of traffic dynamics recurrence and spatiotemporal interaction. However, modeling both dimensions concurrently is challenging, as it demands that the model account for multiple latent factors. To address this, a novel deep learning-based model is proposed that simultaneously accounts for these two dimensions. First, key features with spatial representation value are distilled from the dual perspectives of causal influence and temporal correlation. Second, a sparse slope attention mechanism is designed to capture similar dynamic patterns in historical traffic flows. Finally, multi-scale temporal features are extracted from traffic flows, and a nonlinear activation function is employed to generate dynamic calibration factors, thereby reducing prediction biases across different date distributions. Experimental results on real-world air traffic datasets demonstrate that the proposed approach not only achieves superior performance in the ATFP task but also validates the effectiveness of each technical module in enhancing overall predictive accuracy. We believe the proposed model can serve as a valuable tool for airspace management, providing reliable decision support for air traffic controllers.</p>