In view of multi-quality variation for precision products with parameter uncertainty and unknown model structure, while considering the demand of main structural on lightweight design, a multi-quality robust design method based on Taguchi couple with back propagation neural network and super efficiency data envelop analysis (Taguchi-BPNN-SEDEA) is proposed. Firstly, the multi-quality value is obtained through orthogonal experimental design and finite element analysis. The Taguchi method is adopted to converse the multi-quality value into signal-to-noise ratio(SNR), which can measure the stability of precision product. Then, the multi-quality SNR prediction model is constructed by the non-parametric model of the back propagation neural network(BPNN) to solve errors of model misspecification. On the basis of the improved data envelop analysis, the super efficiency data envelop analysis is proposed to convert the BPNN non-parametric model of the parameter optimization problem into the complex multiple attribute decision making problem under uncertainty. Finally, an example is given to illustrate that the proposed method is feasible and can handle multi-quality robust optimization with the parameter uncertainty and unknown model structure.