Convolutional neural networks (CNNs) havedemonstrated strong performance in visual recognition tasks,but their inherent reliance on regular grid structures limitstheir capacity to model complex topological relationships andnon-local semantics within images. To address this limita tion, we propose the hierarchical graph feature enhancement(HGFE), a novel framework that integrates graph-based rea soning into CNNs to enhance both structural awareness andfeature representation. HGFE builds two complementary levelsof graph structures: intra-window graph convolution to cap ture local spatial dependencies and inter-window supernodeinteractions to model global semantic relationships. Moreover,we introduce an adaptive frequency modulation module thatdynamically balances low-frequency and high-frequency signalpropagation, preserving critical edge and texture informationwhile mitigating over-smoothing. The proposed HGFE moduleis lightweight, end-to-end trainable, and can be seamlesslyintegrated into standard CNN backbone networks. Extensiveexperiments on CIFAR-100 (classification), PASCAL VOC,and VisDrone (detection), as well as CrackSeg and CarParts(segmentation), validated the effectiveness of the HGFE inimproving structural representation and enhancing overallrecognition performance.