For the problems such as poor unmixing results and sensitivity to noise of traditional Nonnegative Matrix Factorization (NMF) applied to hyperspectral unmixing, a Manifold Regularized Sparse NMF with superpixel (MRS-NMF) algorithm for hyperspectral unmixing was proposed. Firstly, the manifold structure of hyperspectral image was constructed by superpixel segmentation based on entropy. The original image was divided into k-superpixel blocks, and the data points in each superpixel block with same property were labeled the same label. Weight matrices were defined between any two data points with the similar label in a superpixel block, and then the weight matrices were applied to the objective function of NMF to construct the manifold regularization constraint. Secondly, a quadratic parabola function was added to the objective function to complete the sparse constraint. Finally, the multiplicative iterative update rule was used to solve the objective function to obtain the solution formulas of endmember matrix and abundance matrix. At the same time, maximum iteration times and tolerate error threshold were set to get the final results by iterative operation. The proposed method makes full use of spectral and spatial information of hyperspectral images. Experimental results show that on synthetic data the unmixing accuracies of endmember and abundance based on proposed MRS-NMF are 0.016-0.063 and 0.01-0.05 respectively higher than those based on traditional methods like Graph-regularized L1/2-Nonnegative Matrix Factorization (GLNMF), L1/2NMF and Vertex Component Analysis-Fully Constrained Least Squares (VCA-FCLS); while on real hyperspectral images, the average unmixing accurary of endmember based on proposed MRS-NMF is 0.001-0.0437 higher than that of traditional GLNMF, Vertex Component Analysis (VCA) and Minimum Volume Constrained Nonnegative Matrix Factorization (MVCNMF). This proposed algorithm improves the accuracy of unmixing effectively with good robustness to noise.