Crassostrea angulata is the main variety of marine aquaculture in southern China. Due to long-term artificial breeding with no germplasm protection measures, its germplasm resources are declining, which has a negative impact on the oyster consumption market. Therefore, it is urgent to develop breeding of Crassostrea angulata (C. angulata). The selection for C. angulata with good nutrition and good flesh quality requires a large number of samples in the nutrient analysis. Traditional laboratory chemical method is time-consuming and costly, so we are looking for an efficient method for determining the chemical content of C. angulata. The spectroscopic scan was carried out using 105 frozen-dried and grinded C. angulata samples (removed the adductor muscle) from six regions with the Fouriernear-infrared spectrometer (Thermo Fisher, USA) in this study. By comparing the spectroscopic scan data to the chemical values, the accuracy of the content predictions of protein, glycogen, taurine, zinc, selenium and calcium in C. angulata obtained by near-infrared spectroscopy (NIRS) was studied. Using TQ Analyst (Thermo Fisher, USA) software, and selecting partial least squares (PLS), spectral preprocessing method like multiplication scattering correction (MSC), 1st derivative, and Norris derivative filter, the near-infrared models of the six components were established. And 1/3 of the total samples were selected as validation samples. The models were validated by external and cross validation. The correlation coefficients of calibration (RC) of the six models of protein, glycogen, taurine, zinc, selenium and calcium were 0.985 3, 0.965 1, 0.950 4, 0.955 4, 0.920 0 and 0.925 2, respectively. The correlation coefficients of prediction (RP) were 0.985 1, 0.963 6, 0.944 1, 0.946 1, 0.919 0 and 0.924 1, respectively. The correlation coefficients of cross validation (RCV) were 0.981 7, 0.946 1, 0.900 5, 0.897 5, 0.875 3 and 0.829 2, respectively. The results showed that the predicted values of the models had a high correlation with the chemical values, which indicated the NIRS could accurately predict the contents of protein, glycogen, taurine, zinc, selenium and calcium in C. angulata. The samples in this study had good representativeness. The collection time was long. The production area was wide and the quantity was large. The samples were frozen-dried, which reduced the influence of water on the spectral quality. Thus, the accuracy and stability of the models were improved. Spectroscopic scan and analysis based on NIRS was very efficient with no chemical reagents and low cost. The established quantitative model for 6 chemical compositions in C. angulata by NIRS would have a great significance for large-scale analysis of the nutritional compositions and for the selection of new strains with good flesh quality in C. angulata.