In this paper, we proposes a novel fuzzy control for parameter scheduling of the Hopfield neural network. When a combinatorial optimization problem, such as the traveling salesman problem, is solved by Hopfield neural network, it is efficient to adaptively change the parameters of the energy function and sigmoid function. By changing the parameters on purpose, this network can avoid being trapped at a local minima. Since there exists complex relations among these parameters, it is difficult to analytically determine the ideal scheduling. First, we investigate a bad scheduling to change parameters by simple experiments and find several rules that may lead to a good scheduling. The rules extracted from the experimental results are then realized by fuzzy control. By using fuzzy control, we can judge bad scheduling from vague network stages, and then correct the relations among the parameters. Computer simulation results of the Traveling Salesman Problem (TSP) is considered as an example to demonstrate its validity.