Ginkgo fruit with antioxidant,anti-tumour and cardiovascular disease prevention functions is rich in vitamins,ginkgo lactones and ginkgo flavonoids,and can be used for both medicine and food.Due to the different varieties of Ginkgo fruit,the content of the main ingredientsis different and there are differences in quality.In addition,the content of certain components in ginkgo fruit has a greater impact on their storage and processing.In order to achieve efficient and non-destructive identification of ginkgo fruit varieties,the Support Vector Machine (SVM) classification model based on hyperspectral imaging technology was proposed,and Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) was used to optimizethe parameters of the model to improve the accuracy of species identification.In this study,630 ginkgo fruits of three species were regarded as the research objectsand divided into training and test sets according to 2∶1,with 420 and 210 samples respectively.The hyperspectral acquisition system acquired Ginkgo fruit images in the range of 900～1 700 nm. Then region of interest (ROI) of 25×25 pixel in the center of mass position was selected after black and white correction,and the average spectrum in the region was extracted as the original spectral data.Because of the large noise at both ends of the original spectra,the signal noise ratio was lower and the effective information was less.The spectral band in the range of 945.98~1 698.75 nm was intercepted as the effective band,which was pre-processed by Standard Normal Variate transformation (SNV).Successive Projection Algorithms (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to extract the characteristic wavelengths.The wavelength reflectivity was used as the input matrix X,and the sample varieties 1,2,3 were used as the output matrix Y. Six identification models were established for the SNV-SPA/CARS-(GA/PSO)-SVM.The experimental results showed that the SNV-CARS-PSO-SVM model had the best identification performance, and the classification accuracy was 96.67%,indicating that the characteristic wavelength variables extracted by CARS could represent all wavelength information,and the PSO-SVM model had a better species identification effect, which could realize the identification of ginkgo fruit.This study provides a new idea for the efficient and non-destructive identification of ginkgo fruit species.