Indefinite kernel support vector machine (IKSVM) has recently attracted increasing attentions in machine learning. Since IKSVM essentially is a non-convex problem, existing algorithms either change the spectrum of indefinite kernel directly but risking losing some valuable information or solve the dual form of IKSVM whereas suffering from a dual gap problem. In this paper, we propose a primal perspective for solving the problem. That is, we directly focus on the primal form of IKSVM and present a novel algorithm termed as IKSVM-DC for binary and multi-class classification. Concretely, according to the characteristics of the spectrum for the indefinite kernel matrix, IKSVM-DC decomposes the primal function into the subtraction of two convex functions as a difference of convex functions (DC) programming. To accelerate convergence rate, IKSVM-DC combines the classical DC algorithm with a line search step along the descent direction at each iteration. Furthermore, we construct a multi-class IKSVM model which can classify multiple classes in a unified form. A theoretical analysis is then presented to validate that IKSVM-DC can converge to a local minimum. Finally, we conduct experiments on both binary and multi-class datasets and the experimental results show that IKSVM-DC is superior to other state-of-the-art IKSVM algorithms.