To address the performance bottleneck of contour-based gait recognition, where over-reliance on global representations leads to sharp degradation under strong appearance interferences like clothing changes, this paper proposes a model integrating structure awareness and dynamic attention. The model aims to elevate the recognition paradigm from matching variable contours to understanding intrinsic motion patterns. To achieve this, this study construct a dual-path parallel framework: First, a structure-aware path precisely models key local body regions; second, a frequency-decoupled dynamic attention mechanism is introduced to adaptively enhance the most discriminative feature channels against gait phase variations; finally, a deep semantic fusion module synergizes local structural information with global representations at multiple scales to generate a final feature with both stability and discriminative power. Experimental results show that the model achieves an average accuracy of 89.9% on the CASIA-B dataset, with 11.0% improvement over the baseline under the changing-clothes condition, and a Rank-1 accuracy of 89.5% on the large-scale OU-MVLP dataset. This study confirms that by synergizing local structure perception and global feature enhancement, the proposed model effectively improves the robustness and accuracy of gait recognition under complex appearance interferences.