In order to further improve the efficiency of forest(plantation) vegetation extraction based on remote sensing image,this paper takes Landsat8 OIL as experimental data on the Google Earth Engine(GEE) platform,and uses supervised classification,support vector machine,maximum entropy model,random forest and decision tree classification based on the actual construction of the experimental area.Line extraction and comparison of various methods are made.On this basis,the area of Eucalyptus in Guangxi was extracted by decision tree,and the experimental results were validated by Unmanned aerial vehicle image and Google Earth Pro historical image.The experimental process and results show that remote sensing vegetation information can be extracted efficiently and quickly by using GEE.Among the five methods in this paper,the decision tree classification method achieves the best results.The overall accuracy and Kappa coefficients of Eucalyptus extraction in the experimental area are 0.82 and 0.85,respectively.At the same time,the area of Eucalyptus extracted by decision tree in Guangxi is in good agreement with the statistical data,which shows that the decision tree classification method constructed in this paper has a good consistency with the results of large area.Rapid extraction of vegetation cover information in complex mountainous areas is of reference significance.