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Hyperspectral technology is widely used in agricultural natural resources such as agro-ecological environment and land resource protection. Spectral imaging technology can effectively classify and identify ground objects. Therefore, the classification of hyperspectral data is one of the important contents of hyperspectral research. Category non-equilibrium problem is a common problem in agricultural hyperspectral data, and the classification quality of minority classes has great significance for the effective classification of hyperspectral data. However, the classification of minority classes is affected by the dominant majority classes. The general classification algorithm tends to the dominant majority classes classification, so that minority classes are usually submerged in the majority classes, bringing great challenge to classification accuracy and recall rate of the minority classes. The classification quality of the minority objects was studied in agricultural hyperspectral data. In order to improve the classification quality of minority classes, an oversampling technique SMOTE was proposed to add new samples for the minority classes. At the same time, the effects of new sample generation strategy and minority instance sampling rate on the classification results of minority samples in the agricultural hyperspectral data and the matching degree between the classifier and the model on the unbalanced data set were systematically studied. A multi-class classification SVC technique was used to classify minority classes on a new sampling data set, and it improved the classification accuracy of the minority classes in unbalanced agricultural hyperspectral dataset. The experimental verification was carried out on the real data set, and different classification methods and system parameters were tested and compared. The experimental results showed that the proposed method can greatly improve the effect of minority classification in unbalanced agricultural hyperspectral data. The weight precision can reach above 0.82, the weight recall rate was obviously improved from 11.11% to 26.15%，and F1 was increased from 5.81% to 40.85%. The method can provide a reference for the unbalanced agricultural hyperspectral data to improve the classification effect systematically.