Remote sensing images with medium spatial resolution and multiband can provide data source for crop classification on country scale. Based on analysis of the spectral characteristics of single image and its vegetation index, the identification and acreage extraction of major autumn crops can be effectively achieved. Taking Puyang County, Henan Province as study area, and basic image with 13 bands and spatial resolution of 10m, which was collected on August 6th, 2017 was employed. Combined with the ground samples and sample points data, the spectrum curve characteristics, including NDVI and RENDVI of the major crop types (corn, peanut, soybean, rice and minor crops such as vegetables, sweet potato, etc.) in this growth period were extracted. Through the analysis of spectrum curve characteristics of different crop types, it can divide the basic image into different regions by building decision tree, which was built by the threshold segmentation of vegetation index features, and band math tool in ENVI software. Based on curve characteristics of NDVI, the basic image data can tripartite regions such as major crop planting region, noncrop planting region and minor crops planting region. Then on the basis of major crop planting region image and its RENDVI data, it can divide this region into two regional images, including corn/rice and peanut/soybean. Finally, synthesizing the above results, five crop types in the study area were classified by SVM and limited training samples. The precision of the results by using decision tree and SVM was evaluated compared with ML and SVM methods, which were gradually adjusted according to the validation of field samples and sample points. The method can effectively solve problems such as incomplete extraction of linear object, different crops in small plots, and also phenomena of “salt and pepper”. Its overall accuracy and Kappa coefficient reached 92.3% and 0.886, respectively. The precision of classification can meet the demand of remote sensing image classification by analyzing spectral characteristics and vegetation index. Based on mono temporal Sentinel-2A data, it can provide data support and technical reference for regional complex crop classification extraction.