Fusarium head blight (FHB) is a major disease of wheat, which can lead to wheat yield reduction or even crop failure, seriously affecting the quality of wheat seeds. Mycotoxins secreted by the diseased wheat are deposited in the food chain, and ultimately endanger human health. Therefore, recognition of wheat scab is very important. First of all, chromatography and enzyme-linked immunosorbent assay (ELISA) are widely used to detect scab. These methods were expensive, slow and have low accuracy. In recent years, hyperspectral imaging technology has been widely used in crop identification and detection, but in the application of wheat scab detection, sampling detection method is mostly used. After image acquisition, the region of interest (ROI) is manually selected through ENVI software. Preliminary preparations are complicated and easy to be missed detection. The undetected wheat grains quickly infect the surrounding grains during storage and transportation, which is difficult to ensure the safety and health of wheat. In view of this, this paper presents a fast visual recognition algorithm for wheat scab samples based on hyperspectral imagery and machine learning to reduce the rate of missed detection and improve the detection efficiency. The hyperspectral images of healthy wheat and infected wheat in 469~1 082 nm band were collected, and the mask image information of wheat samples was accurately obtained by histogram linear stretching combined with image segmentation. Savitzky-Golay smoothing denoising method and standard normal variable transformation (SNV) method are used for data preprocessing. Principal component analysis (PCA) and successive projections algorithm (SPA) were used to extract features, and 4 and 8 feature variables were selected respectively. There were 400 healthy wheat samples and 400 infected wheat samples collected in the mask image position, 75% of which were used for modeling set and 25% for testing set. Ten fold cross validation method combined with linear discriminant analysis (LDA), K-nearest neighbor algorithm (KNN) and support vector machine (SVM) was used to establish the classification model. The accuracy of the test set is over 90%, and the SPA dimension reduction model is better than the PCA dimensionality reduction model. Then, the effects of GRID, particle swarm optimization and GA three kernel parameter optimization methods on SVM model are compared. Among them, SG-SPA-SVM (PSO) model has the best classification effect. The accuracy of modeling set is 95.5%, REMS is 0. 2121, the accuracy of test set is 98%, and REMS is 0.141 4. Based on the prediction of sample points, the spectral curves of all wheat samples obtained by the mask were predicted and the recognition results were fed back to the mask and displayed in pseudo-color to realize the visual identification of infected grains. The results show that the classification model based on hyperspectral imaging technology combined with SG-SPA-SVM (PSO) algorithm can effectively, quickly, accurately, nondestructively and visually identify wheat scab, providing an algorithm basis for the development of automatic identification equipment for wheat scab.