This paper proposes a hyperspectral unmixing algorithm based on the weighted residual synergetic graph constraint NMF(WRGNMF), building upon the foundation of sparse graph constraint NMF. In the standard NMF algorithm, a residual weighting mechanism is introduced. This strategy processes the values in the residual according to the weighting factor, providing appropriate weights for the reconstruction errors between each original pixel and the reconstructed pixel during the unmixing process, thereby enhancing data fitting accuracy and improving noise resistance. Simultaneously, to fully utilize the spatial information of spectral images, the l₁ norm is employed to enhance the sparsity of the abundance matrix, and graph regularization is used to maintain the affinity of the data structure. Both simulated and real experiments demonstrate the effectiveness of the proposed algorithm, which not only improves unmixing accuracy but also exhibits greater robustness to noise.