In order to study the application potential of tree species recognition based on unmanned aerial vehicle (UAV) visible image with LiDAR individual tree segmentation aided, a tree species recognition method combined with convolutional neural network and ensemble learning was proposed. Firstly, individual trees were detected by means of individual tree segmentation of simultaneous UAV-LiDAR point clouds and multiscale segmentation of UAV visible image, and then individual tree canopy image datasets was sliced from UAV visible image. Secondly, ResNet50 convolutional neural network was introduced, meanwhile, a ECA-ResNet50 network was bulit by using ResNet50 as the backbone network framework and inserting the effective channel attention (ECA) mechanism model to residual bottleneck module, and then a ECA-ResNet50-Dialate network was bulit by replacing normal 3×3 convolution of residual module with dilated convolution, and ECA-ResNet-mini and ECA-ResNet-mini-Dialate network were bulit by adjusting the convolution layer number of convolutional modules in the end. The pre-trained model parameters, which were pre-trained by using ImageNet datasets, were loaded to initialize the five network models, after that five recognition models were trained by using the individual tree canopy image datasets. Finally, the five convolutional neural network models were ensembled by the relative majority voting method. The experimental results showed that the overall accuracy of individual tree detection was 83.80%, and the training, verification and independent test accuracy of ensemble learning reached 99.15%, 98.34% and 90.15%, respectively, which were 4.23, 3.04 and 9.09 percentage points higher than that of ResNet50 network, and the independent test accuracy was still 32.31 percentage points higher than the traditional optimal result of random forest classification. The combination of convolutional neural network and ensemble learning strategy could fully extract UAV visible image features for tree species recognition with LiDAR individual tree segmentation aided.