According to application of neural network to solving recurrent optimization problems under linear and nonlinear constraints，this paper proposes a simplified recurrent neural network for solving quasiconvex optimization problems under nonlinear programming.The quasiconvex optimization problems on complex domain are transformed into the optimization problems on real number domain by defining auxiliary function，the corresponding neural network model is derived，and the stability and convergence of the equilibrium solution of this neural network is proved by established Lyapunov function.For arbitrary initial point，this neural network is Lyapunov globalstable and the optimal solution converging at optimization problem.Numerical example verifies the validity of this method and the correctness of the conclusion.