The mass concentration detection of trace copper ion in zinc solution is difficult because of trace copper spectral signals masking, serious interference and significant nonlinearity difference of copper ion in the high and low mass concentration intervals. Aiming at this issue, we propose a spectrophotometric detecting method of trace copper ion in zinc solution based on partition modeling. The derivative spectrum combined with wavelet denoising is used to preprocess the spectral signal and reproduce the spectral peak of the copper ion to be measured. The wavelength variables are ranked by the correlation coefficient-stability value, which serves as the evaluation index of the variables, and the support vector regression (SVR) model is used to select the optimal wavelength variables. On this basis, the mass concentration of the copper ions is divided into several intervals according to the nonlinear characteristics in the mixed solution. The particle swarm optimization support vector regression (PSO-SVR) model is respectively established for each interval to compute the concentration of copper ions. The proposed method is compared with many existing regression methods. The results show that the predicted root mean square error obtained with the proposed method is reduced to 0.0678, and the model determination coefficient is increased to 99.61%. The maximum relative error obtained with the method is 6.94% and the average relative error is 2.74%.