The invention discloses a multi-source remote sensing data classification method for extracting classification sample points based on an unmanned aerial vehicle, and the method comprises the steps: uniformly extracting the classification sample points from aerial photos of the unmanned aerial vehicle, and carrying out the preparation and calibration of each type of sample points; obtaining a classified remote sensing data set, performing image processing on the remote sensing data set, and performing geographic positioning on the classified sample points according to the classified remote sensing image data set, wherein the classified remote sensing data set comprises a microwave data Sentinel-1 data set, a multispectral Sentinel-2 data set, a vegetation index data set based on the Sentinel-2 data set and a digital elevation model data set; and obtaining a classification result by utilizing a random forest classification model through the classification sample points with the geographic space information positioning. According to the multi-source remote sensing data random forest classification method based on the classification sample points extracted by the unmanned aerial vehicle, the earth surface type classification drawing process can be rapidly, effectively and cheaply realized; and meanwhile, after the influence of edge classification sample points is eliminated, the classification precision is obviously improved, and particularly, the precision of the kappa coefficient is better.