To explore the application potential of unmanned aerial Vehicle (UAV) remote sensing images for subtropical tree species recognition, the ECA-ResNet with residual module and effective channel attention is proposed to train and recognize single tree crown images. First, the single tree crown was extracted by single-tree segmentation algorithm. The single-tree crown image patch dataset of UAV visible image was constructed by means of clipping images with different window sizes, and they were divided into training data, validating data, and independent test dataset respectively. Second, with ResNet50 as a backbone network, by inserting effective channel attention into ResNet bottleneck and adjusting network structure, the ECA-ResNet was constructed. Then, the datasets were inputted into pretrained ECA-ResNet model for parameter training and validation iteratively, and independent test. After that, the optimum window size of single-tree crown image was determined. The results show that the ECA-ResNet gets a better recognition result for tree species in single-tree crown image patch dataset with window size of 64×64 pixel, the accuracy of training and validation of the proposed network reaches 98.98% and 96.60%, respectively. The recognition accuracy and Kappa coefficient of independent test reach 85.61% and 0.8140. The training, validation, and independent test accuracy of ECA-ResNet in this paper are 2.63 percentage points, 1.80 percentage points , and 5.31 percentage points higher than that of the ResNet50 respectively. It is proved that, convolutional neural network (CNN) can fully extract the spatial features of UAV visible images for tree species recognition, effective channel attention can effectively improve CNN' single tree species recognition capability.