Summary: A predictor-correct interior point method for solving convex quadratic programming problems with box constraints is presented. Actually, the method is equivalent to solving a system of equations -- the first order optimality conditions of the problem by decomposing one Newton step with one simplified Newton step, and has a nice convergence property of high order. Moreover, the center direction generated by introducing the barrier parameter is used to correct the descent Newton direction such that the search direction which consists of the center and Newton direction avoids hitting the board of the feasible region. So that the iterative sequence generated by the algorithm remains inside of the feasible region and converges to the optimal solution. Furthermore the numerical results for a group of test problems are given, showing that the algorithm works very efficiently.