At present, the analysis of copper grades at home and abroad is mainly based on chemical analysis. Due to the disadvantages of high cost, long time and residual pollutants, the chemical analysis method has a serious hysteresis effect on the relative ore blending process, resulting in that the copper content of the tailings is too high, which will inevitably lead to waste of resources. It is an effective way to solve this problem by conducting visible-near-infrared spectroscopy and modeling of porphyry copper deposits. Based on the chemical analysis and spectral test data of 121 Wushan porphyry copper deposits, the visible-near-infrared spectral characteristics of porphyry copper deposits are analyzed. The original spectral data is processed by principal component analysis (PCA) and local linear embedding algorithm (LLE). The reduced dimension is 3 and 5 dimensions respectively. At the same time, the genetic algorithm (GA) is used to select the band of the original spectral data. A total of 467 optimal bands are selected. Then, this paper takes the BP neural network as the modeling method, respectively uses visible-near-infrared spectroscopic data of 92 and 29 porphyry copper deposits as the modeling and testing samples, and establishes a quantitative inversion model of visible-near infrared spectroscopy for porphyry copper deposits. The average absolute error of the grade inversion model based on the original data is only 0.104%. The average absolute error of the grade model based on the model processed by the principal component analysis method, the locally linear embedding algorithm and the genetic algorithm is 0.110%, 0.093% and 0.045% respectively. It can be seen that the grade inversion accuracy of the model based on the data processed by principal component analysis method is poor, the accuracy of grade inversion model based on locally linear embedding algorithm is slightly improved, and the grade inversion accuracy of the model based on the data processed by the genetic algorithm is improved obviously. The research result shows that the grade analysis based on the inversion model of visible-near infrared spectroscopic data of low-grade porphyry copper deposits is feasible, providing an effective method for rapid grade detection of low-grade porphyry copper deposits in China.