An improved hyperspectral unmixed initialization method (IISSF) based on non-negative matrix factorization (NMF) combining Euclidean distance and spectral information divergence is proposed. On the basis of initialization, a parallel comparison experiment is performed in combination with the standard NMF algorithm and the block NMF algorithm. The results show that, in the synthetic image experiment, the block NMF algorithm after IISSF initialization is better than other methods in the signal-to-noise ratio range from 20 dB to 50 dB. There is a minimum average spectral angular difference between the end-member spectrum obtained in the real image experiment and the reality image endmember spectra, i.e., 0.1812. The root mean square error between the reconstructed image and the real image is the smallest, i.e., 0.007.