Rapid and non-destructive detection of crop disease types are essential to improve crop quality and yield. Traditional disease classification methods are time-consuming and difficult to detect in real-time. Therefore, the classification of soybean diseases was carried out by the hyperspectral technique. In this paper, healthy soybean was used as the control, frogeye leaf spot and bacterial blight diseases were the research objects, and hyperspectral data of three types of leaves were obtained. Changes inthe reflectance of diseased and healthy leaves were analyzed based on hyperspectral curves. Two single methods, principal component analysis (PCA) and spectral index (SI), were used to extract effective disease information. A total of 30 SI were used. A combination method of PCA and SI (PCA-SI) was proposed on this basis. Extracting the effective principal component (PC) and the effective SI, which were divided into two groups (9SIs and 18SIs) according to the score, and then grouped corresponding to each effective PC respectively to form the variable set of effective information of the disease spectrum. Three methods were used to extract effective disease information respectively. Based on the extracted spectral variables, the least square support vector machine (LSSVM) and support vector machine (SVM) was used to establish the disease classification model. With the original hyperspectral as the benchmark and the accuracy of disease classification as the index, the disease classification performance of the model, the effective information extraction methods of different diseases and the effectiveness of the classifier were evaluated. The results showed that the hyperspectral reflectance of diseased leaves was higher than that of healthy leaves in the visible band of 450~700 nm, while the characteristics of diseased leaves were opposite in the near-infrared band of 760~1 000 nm. A single PCA method was used to extract 34 effective PCS for disease classification. Based on the PCA-SI combination method, 5 effective PCs (PC1—PC5) and 18 effective SIs were extracted and grouped to obtain 10 groups of variables, and 13 groups of variables were used as modeling sets. The spectral variables extracted by the three methods have better disease classification ability than the original hyperspectral, and the proposed PCA-SI combination method has the optimal disease-effective information extraction ability. PC1-18SIs and PC4-18SIs were the best modeling sets, and the LSSVM classifier performed the best classification. PC1-18SIs-LSSVM and PC4-18SIs-LSSVM models were the optimal disease classification models. The total disease classification accuracy of the training and prediction sets was 100% and 98.85%, respectively. Compared with the original hyperspectral classification model, the overall classification ability of these two models was improved by 6.47% and 21.74%, respectively, and the model classification ability was good. It can provide a reference for real-time and non-destructive classification and identification of diseases.