In recent years, graph convolutional network (GCN) has garnered increasing attention in hyperspectral image (HSI) classification. However, due to the excessive redundancy in message passing of GCN within dense graph structures, the superpixel adjacency matrices constructed by researchers tend to be sparse. This in turn leads to a scarcity of neighborhood information acquired by nodes. In addition, in complex HSI environments, the randomness of graph structures and training samples introduces interference, thereby increasing classification difficulty. To address these issues, we propose an HSI classification algorithm called robust dense graph structure based on GCN (DGGCN). The algorithm reduces computational burden by introducing a superpixel segmentation algorithm and constructs dense graph structures to enrich neighborhood information. Then, a dynamic fusion algorithm and a spatial and feature difference weighting algorithm are employed to reduce the confusion of message passing in GCN and decrease information redundancy, thus effectively addressing the aforementioned problems. Finally, we evaluate the DGGCN method on three public HSI datasets, demonstrating its superiority over other advanced classification methods in terms of overall accuracy, average accuracy, and kappa coefficient.