Global climate change poses a threat to food development and sustainable development of agriculture. As one of the world's most significant food crops,rapid and accurate information extraction of it is of great importance in ensuring food stability and security.In this paper,the random forest algorithm with obvious advantages in crop identification and extraction was selected.The feature selection and rapid extraction of winter wheat plots based on 30mspatial resolution imageswere achieved by combining the spectral features,texture features and the principal components of the typical winter wheat growing areas.The extraction results under different feature spaces combination were compared and analyzed.The results showed that under the combination of three spectral spaces:spectral feature,spectral feature+texture feature andspectral feature+texture feature+principal component feature,the third combination method had the best extraction efficiency and the highest overall accuracy up to 84.85%,respectively higher than the previous two 8.08%and 6.88%.Therefore,by using random forest algorithm combined with multi-source feature information,the rapid extraction of specific crops,such as winter wheat,can be effectively achieved and provide effective data,thus supporting for the further application of regional crops.