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In order to improve the correctness and efficiency of tree species recognition, to slow down the occurrence of overfitting, and to enhance the practicality of tree recognition techniques, this paper proposed a tree recognition method based on an improved convolutional neural network. The approach started with compressing the channel in the Xception framework to further exploit the average pool of global feature mapping, and changing the hybrid attention connection to parallel connection. Next, a randomly selected part of the feature map was normalized and re-entered into the neural network after using the attentional feature map cropping method. Finally, ablation experiment was performed and the accuracy of tree species recognition was up to 98.90% in the tree dataset when the learning rate was 0.1 and the iteration was 50 times. The study showed that the proposed improved convolutional neural network had better recognition effect on tree recognition. And the memory of the convolutional neural network architecture was reduced to 133.9 MB and the time consumed was only 458 ms. Using the improved convolutional neural network not only improved the accuracy of tree recognition, but also reduced the time cost. [ABSTRACT FROM AUTHOR]