The mixed integer linear programming （MILP） model for large-scale security constrained unit commitment （SCUC） problem is difficult to solve because of its high-dimensional and non-convex characteristics, especially when considering post-contingency security constraints, the scale of model increases rapidly, and MILP algorithm often encounters the bottleneck of decreasing convergence gap. In order to meet the requirements in spot market clearing process, a fast solving method based on warm-start is proposed to improve the solve speed of SCUC. To reduce the scale of model and speed up the convergence process, the method starts with a feasible solution, integer variables are fixed based on locational marginal price and unit benefit analysis, also the non-binding security constraints are reduced. Simulation results show that the proposed method can greatly reduce the scale of SCUC model. Especially for the large-scale SCUC problem with post-contingency security constraints, the method can effectively overcome the bottleneck of decreasing convergence gap and significantly improve the solution efficiency.