In this study, we attempt to use deep learning and object-oriented methods to deal with very-high-resolution visible images obtained from an unmanned aerial vehicle (UAV) for achieving high-precision classification of the forest tree species. First, we use the optimal-scale object-oriented method to segment the images obtained from the UAV. The random forest (RF) method is used to classify the tree species for extracting the feature variables. In addition, the classification variables are sorted based on their importance and significance. Further, the most important feature variables with respect to the classification, including the visible light difference vegetation index (VDVI) and the over-green to over-red reduction index (ExG-ExR), are selected. Subsequently, new data are generated by combining two characteristic variables and the original RGB band of the UAV images. Based on the new data and the original RGB band data are both used to classify tree species by the full convolutional neural network (FCN) method based on the Res-U-Net model. Then, the classification result accuracies in the aforementioned cases are evaluated and compared. Finally, the object-oriented segmentation method is used to correct the optimal tree species classification results. The experimental results denote that FCN with respect to VDVI and ExG-ExR exhibits the best classification effect in case of the original images of the tree species obtained via UAVs. The total accuracy is 97.8%, and the Kappa coefficient is 0.970. RF methoed can effectively screen out the classification feature variables. The addition of characteristic variables to the original image can effectively improve the classification accuracy of the FCN method. Finally, the best classification result is obtained using object-oriented segmentation, resulting in the elimination of the salt and pepper phenomenon and the attenuation of the edge effect. The total accuracy improves by 0.9 percentage points and the Kappa coefficient increases by 0.013.