Multinomial logistic regression (MLR) is an effective classifier in spatial–spectral-based hyperspectral image (HSI) classification. However, in some typical scenarios, such as Gaussian regularized MLR (GSMLR) and Laplacian graph regularized MLR (LPMLR), it hits a large ( cd ) × ( cd ) linear system during the regressors learning procedure that is unbearable in both space and time complexity (c is the number of classes and d is the length of feature). Even if using middle-sized features, it often runs out of memory. To this end, we propose two exact divide-and-conquer (DC) algorithms, DC-GSMLR and DC-LPMLR, to reduce the computation complexity. Both decompose the regressors learning problem into a series of equivalent smaller subproblems, each of which can be solved in closed form. Unlike the approximation ones available, they provide exact merged solutions instead. With the same accuracy, DC-LPMLR and DC-GSMLR only need to solve c + 1 and 2 d × d linear systems, respectively, significantly reducing the peak memory usage by almost O ( c ) and O ( c2 / 2 ) times. For time, experiments on two popular HSI datasets indicate considerable speedup ratio as high as one or two orders of magnitude, showing the practicability in real applications.