This paper proposed a new high-dimension model representation (HDMR) based on back propagation neural network (BPNN), which is called BPNN-HDMR. The most remarkable advantage of this method lies in its ability to integrate the nonlinear function approximation capability of BP neural network and the hierarchy structure theory of high dimensional model to build an approximation model. Moreover, this method can reveal the inherent linearity or nonlinearity relationship as well as correlation with respect to input variables. The problem of modeling high dimension model is effectively tackled by reducing the computation cost from exponential growing to polynomial. Testing and comparative analysis confirm the efficiency and capability of BPNN-HDMR for high dimension nonlinear problems. Furthermore, the algorithm was applied to optimize the ROPS of Mining Dump Truck's Safety Cab. The optimized results verify the feasibility and effectiveness of the method proposed.