Accurate identification of non-precipitation echoes (NPEs) in weather radar observations requires effective use of polarimetric signatures together with spatiotemporal structure. Here we present a unified deep-learning framework to quantify the independent and synergistic contributions of model architecture, dual-polarization variables, and short-term temporal evolution to NPE identification. Using data from the Guangzhou S-band dual-polarization radar, we conduct controlled comparative experiments with two representative architectures: a pointwise multilayer perceptron (MLP) and a Transformer-based Swin U-Net that explicitly learns spatial context. We further perform ablation experiments across single- versus dual-polarization inputs and single-volume versus two-volume inputs. Results show that architecture-driven spatial-context learning is the dominant factor: Swin U-Net consistently outperforms the pointwise MLP under all input settings. On a high-confidence test subset, for example, the Critical Success Index (CSI) increases from 0.887 for the dual-polarization MLP to 0.950 for the dual-polarization Swin U-Net. Dual-polarization variables provide essential microphysical constraints and substantially improve class separability, particularly for pointwise classifiers. Incorporating two consecutive volumes further improves performance by capturing short-term echo evolution, with larger gains for the MLP than for Swin U-Net. The best-performing configuration, combining Swin U-Net with dual-polarization and two-volume inputs, achieves a CSI of 0.953 on the high-confidence test subset. Notably, the Swin U-Net using only the reflectivity factor (<em>Z<sub>H</sub></em>) as input retains strong skill (CSI = 0.927), indicating that spatial-context learning can partially compensate for missing polarimetry and thus providing a practical pathway for quality control of legacy single-polarization archives.