The task of hyperspectral unmixing (HU) is inherently more complex than classification, as it requires separating mixed pixels into pure spectral components, demanding fine-grained spectral and spatial modeling. Traditional convolutional neural networks (CNNs), constrained by local receptive fields, struggle to capture the complex manifold structures and non-Euclidean relationships in hyperspectral images (HSIs). Graph convolutional networks (GCNs) offer a promising alternative by modeling long-range dependencies, but they often rely on static, superpixel-based graphs constructed during preprocessing, limiting their flexibility and accuracy. To address these limitations, we propose a differentiable clustering GCN (DCGCN) for HU. The model integrates spatial neighborhood information with dynamic graph structures, leveraging a differentiable clustering module (DCM) to automatically construct and update the graph during training. This enables adaptive learning of both local continuity and global structural dependencies in an end-to-end framework. To further support benchmarking, we introduce a challenging real-world dataset from the Yellow River Estuary Wetland, along with a reproducible data processing pipeline. By combining GF-5 hyperspectral and GF-6 high-resolution imagery, the dataset provides reliable reference endmembers and abundances without the need for field surveys. Extensive experiments on simulated and real datasets demonstrate that DCGCN outperforms or matches state-of-the-art methods in both accuracy and robustness. Code and dataset will be made publicly available at GitHub: https://github.com/UPCGIT/DCGCN