Quasi-ARX neurofuzzy (Q-ARX-NF) models have shown great approximation ability and usefulness in nonlinear system identification and control. However, the incorporated neurofuzzy networks suffer from the curse-of-dimensionality problem, which may result in high computational complexity and over-fitting. In this paper, support vector regressor (SVR) based identification approach is used to reduce computational complexity with the help of transforming the original problem into Lagrange space, which is only sensitive to the number of data samples. Furthermore, to improve the generalization capability, a parsimonious model structure is obtained by eliminating insignificant input variables for the incorporated neurofuzzy network, which is implemented by genetic algorithm (GA) based input selection method with a novel fitness evaluation function. Two numerical simulations are tested to show the effectiveness of the proposed method.