Tobacco is an important economic crop and source of tax revenue in our country. It makes a huge contribution to the country’s economic development. However, tobacco diseases affect the yield and quality of tobacco leaves seriously. Therefore, It is important that the use spectral analysis technology for early prevention and control of tobacco diseases. Objects of research are tobaccos inoculated with tobacco mosaic virus (TMV) and potato Y virus (PVY). The hyperspectral data of infected tobacco cultivated indoors and outdoors are collected respectively. In order to improve the detection accuracy of tobacco diseases, spectral data of two kinds of diseased tobacco are collected every two days, each disease data is divided into five severity levels in detail, and finally, 1 697 spectral data in the 350~2 500 nm band are obtained. In order to make effective use of hyperspectral tobacco data, this paper is based on a support vector machine (SVM), combined with a fast nearest neighbor band selection algorithm (FNGBS) and normalized matched filtering (NMFW), and proposes a combination of clustering and sorting Band selection algorithm (FNG-NMFW). Firstly, FNG-NMFW uses the FNGBS to group the tobacco spectrum finely and then sorts the groups of bands based on the NMFW algorithm to select the characteristic spectrum and realize the extraction and dimensionality of the tobacco spectrum. After completing the band selection, this paper uses SVM to classify tobacco characteristic spectra and achieves high-precision tobacco disease detection. The research results show that the model has stable performance and high accuracy. When the proportion of training samples is only 40%, an overall accuracy (OA) is better than 80%; when the number of feature bands is selected as 40, OA can be better than 85%. The algorithm can achieve higher accuracy for both TMV and PVY diseases, but the recognition accuracy of TMV is slightly lower than that of PVY. For the monitoring of TMV1 and TMV3, the algorithm can achieve monitoring with an accuracy better than 94%, and for the monitoring of PVY1 and PVY3, the accuracy of the algorithm is close to 90%, which shows that the algorithm can realize the early identification and prevention of two diseases. Compared with the model that uses full-band spectral data for disease detection, the FNG-NMFW model has obvious advantages. The accuracy of tobacco disease detection results is 94.46%, the accuracy is improved by more than 1.5%, and the modeling time is shortened from 12.9 seconds to 1.1 seconds.