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In order to solve the problem of remote sensing image plant community background, only the traditional image processing method based on pixel spectral information fails to make full use of its image feature information, which makes the extraction effect poor. Aiming at the complex features of plant species and the blurring of interclass boundaries, the continuous distribution of regions was taken as the research object. A highresolution remote sensing image plant community automatic classification based on the convolutional neural network (CNN) was proposed. The UAV images were segmented to obtain regular block images, and the features of block images were abstracted and learned by CNNbased VGGNet and ResNet models to automatically acquire deeper abstract and more representative image block deep features. The extraction of the plant community distribution area was performed to output the automatic classification results of the plant community in the form of superposition of the original image and the result image. The number of samples with different gradients was used as the training sample. The influence of the number of training samples with different gradients on the automatic classification results was analyzed by the proposed method. The experimental results showed that the number of training samples had a significant impact on the classification accuracy. After improving its generalization ability, the modeling accuracy of ResNet50 model and VGG19 model was improved from 86.00% and 83.33% to 92.56% and 90.29%, respectively. The classification accuracy of ResNet50 model was varied from 83.53% to 91.83%, while the classification accuracy of the VGG19 model was varied from 80.97% to 89.56%. Compared with the traditional supervised classification method, the deep convolution network significantly improved the classification accuracy. Through the analysis of classification result, it was found that the number of training samples should not be less than 200, and the CNNbased ResNet50 model showed the best classification results.