Video-based gait analysis has become a promising approach for assessing motor impairment in children with cerebral palsy (CP). However, existing methods usually rely on either pose sequences or handcrafted gait features alone, making it difficult to simultaneously capture spatiotemporal motion patterns and clinically meaningful biomechanical information. To address this gap, we propose a multimodal fusion framework that integrates skeleton dynamics with contribution-guided clinically meaningful gait features. First, Grad-CAM analysis on a pre-trained ST-GCN backbone identified the most discriminative body keypoints, providing an interpretable basis for subsequent gait feature extraction. We then build a dual-branch architecture, with one branch modeling skeleton dynamics using ST-GCN and the other encoding gait features derived from the identified keypoints. Fusing the two branches through feature cross-attention improved four-level CP motor severity classification to 70.86%, outperforming the baseline by 5.6 percentage points. Overall, we demonstrate that integrating skeleton dynamics with clinically meaningful gait descriptors can improve both prediction performance and biomechanical interpretability for video-based CP severity assessment.