Abstract: In order to improve the accuracy of visible-near infrared (VNIR) hyperspectral peanut image classification and to reduce the computing time of classification detection, a classification detection model based on successive projection algorithm (SPA) fused with particle swarm optimization back propagation(PSO-BP) neural network was proposed. A hyperspectral imaging system was used to acquire VNIR spectral data of seven peanut species samples and conducts background segmentation and extraction of spectral information. After removing the wavelengths that were highly affected by noise and stray light, the wavelengths in the range of 400 nm~900 nm were preprocessed by using Savitzky-Golay convolutional smoothing. The SPA was used to reduce the dimensionality, and 25 characteristic wavelengths were selected by virtue of the root mean square error values. The PSO was also used to optimize the initial weights and thresholds of the BP neural network, and the PSO-BP model was constructed as a classifier for the experiments, and a recognition accuracy of 98.7%, a kappa coefficient of 0.98, and a miss error of 3 for the test set were obtained. The results demonstrate that the accuracy of the model is improved by 2.1%, 8.6%, 3.9%, and 4.3%, respectively, compared with the classification models constructed by the four comparison methods. The proposed method has good application prospects in peanut variety classification based on hyperspectral imaging technology, and provides a new idea for high accuracy and fast nondestructive classification of peanut varieties.