The Peacaman-Rachford splitting method (PRSM) is efficient method for separable optimization problem. However, it's not converge in general for convex problem. In this paper, we discuss an inertial proximal Peaceman-Rachford splitting method (IPPRSM) for a type nonconvex and nonsmooth optimization problem with linear constraints. This method uses the basic idea of the inertial proximal point method. When applied to minimizing the sum of a strongly convex Lipschitz differentiable function and a proper lower semicontinuous function, if the strongly convex function has a large enough strong convexity modulus, the penalty parameter is chosen above a threshold that is computable, we show that the global convergence of the IPPRSM. We then propose a way to solving optimization problems with an objective that is the sum of a hypoconvex (not necessarily strongly) Lipschitz differentiable function and a proper lower semicontinuous function.