The new and old characteristics of pinus koraiensis seeds is an important property reflecting the edible value and breeding value. The pinus koraiensis seeds with a short storage period also have high deep processing value. However, it is difficult to distinguish by appearance, weight and texture. At present, traditional biochemical methods are used to detect the chemical properties and germination percentage of pinus koraiensis seeds to judge their new and old quality. It takes a long time to meet the needs of online detection, and improper treatment of chemical reagents can cause environmental pollution. Near-infrared spectroscopy (NIRS) is widely used in the field of food detection and forestry. Therefore, it has practical significance and guiding significance for qualitative analysis of nuts with shells. In this study, near infrared spectroscopy was used to conduct nondestructive testing of pinus koraiensis seeds matured in the current year and in previous years. Firstly, the 120 pinus koraiensis seeds were randomly selected and labeled according to new and old classifications. In order to reduce the leakage of light during the measurement process and make the experimental data more generally, the near-infrared diffuse reflectance spectra of pinus koraiensis seeds samples on the same side were collected uniformly. Then, the original spectrum was pretreated by using a standard normalized variable (SNV), first derivative and Savitzky-Golay (SG) algorithm, so as to reduce the influence caused by human factors and pretreatment in the experiment process, and highlight the characteristic information of the near-infrared spectrum. After that, principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) were used to reduce the dimension of the pretreated data and compare the effect of dimension reduction. Through the visualization of the data and the output of the clustering parameters, a better dimension reduction scheme was obtained by comparison. The non-linear dimensionality reduction method has a good effect in the near-infrared spectral data processing of pinus koraiensis seeds. Therefore, the t-SNE method was used to reduce the dimensionality of the data, and the optimal characteristic variables were obtained. Finally, taking the reduced dimension data as input. Using two-thirds of the sample data as a correction set to establish a support vector machine (SVM) correction model for classification of new and old seeds, and a third of the sample data were used as a validation set to validate the model performance. The results indicate that. The superposition of SNV, first derivative and SG to pretreat the spectrum can effectively eliminate the noise, it makes the absorption peak more obvious. Meanwhile, it also makes the spectral profile clearer and smoother, which is more conducive to the establishment of the later model. The method of t-SNE is used to reduce the data to two-dimension as the input of the classification model, and when the kernel function selects the RBF, the value of K is 5, γ is 82.54 and the penalty coefficient C is 383.12, the SVM classification model has the best classification effect, the accuracy can reach 97.5%, and the average time consumption is 0.02 s. Near-infrared spectroscopy can be used to achieve non-destructive testing of the new and old characteristics of pinus koraiensis seeds.