A multimodal gait recognition method based on skinned multi-person linear (SMPL) modal decomposition and embedding fusion was proposed, to address the limitations in current gait recognition research, including insufficient gait information mining and inadequate cross-modal feature alignment that restrict recognition performance in real-world scenarios. The SMPL model was decomposed into a shape branch and a pose branch to comprehensively extract static body shape features and dynamic motion characteristics. An adaptive frame-joint attention module was constructed to focus on key frames and significant joints adaptively, thereby enhancing pose feature representation. A modality embedding fusion module was designed to project different modal features into a unified semantic space, and a modality consistency loss function was built to optimize cross-modal feature alignment and improve fusion effectiveness. Experimental results on the Gait3D dataset demonstrated that the proposed method achieved a Rank-1 accuracy of 70.4%, outperforming six silhouette-based methods, two skeleton-based methods, and five multimodal approaches combining silhouettes with skeletons or SMPL models. The method exhibits superior robustness in complex real-world scenarios, validating its effectiveness in modal feature extraction and cross-modal feature alignment.