Using neural networks to solve combinatorial optimization problems is an effective approach. Analyzes the mathematical model and stability of continuous Hopfield neural network, discusses the use of CHNN to solve combinatorial optimization problem and puts forward the improved algorithm aiming at the insufficiency of the traditional method of parameters configuration complex and convergence rate slower. Finally, through the system simulation and performance testing, demonstrates the algorithm feasible.