【Objective】 In order to examine the feasibility of developing a deep learning method for the intelligent identification of forestry tree species, we propose a new method of automatic tree recognition tree based on deep learning. In this study, we improved a convolutional neural network model under the framework of TensorFlow, and used this to examine the automatic identification of images of seven tree species. 【Method】 Initially, when creating an image library, in order to increase the feature selection diversity, the bark and leaf images of the trees are selected to preserve the natural background. In addition, considering that the same tree species can have different bark images depending on tree age, the images of bark from trees of different ages are added, and the DBH index is used to indicate the age of the tree. Thereafter, 100 images of each tree species were randomly selected as test sets, and the remaining data sets were all used as training sets. Through repeated experiments to compare the effects of different convolutional neural network structure settings, the number of convolutional layers, the number of fully connected layers, and the learning rate on the experimental results, we propose the use the Adam algorithm instead of the traditional SGD algorithm to optimize the model and use the index. The attenuation method adjusts the learning rate. The L2 regular term is added to the cross entropy function to penalize the weight, and the Dropout strategy and ReLU excitation function are used. Finally, a 13-layer convolutional neural network structure suitable for the experimental requirements of this study was determined. In addition, in order to compare differences between the deep learning method and the traditional artificial feature recognition method, we compared performance with that of an existing tree species image recognition method.【Result】 The 13-layer tree species image recognition model proposed in this study achieved an ideal recognition effect on both training and test sets, with recognition rates of 96.78% and 91.89%, respectively. Furthermore, we obtained an average 96% recognition for the verification set that was not included in the training, and overall found the recognition efficiency and accuracy to be higher than those of the existing artificial feature recognition methods. 【Conclusion】 Compared with the traditional method, the proposed method, based on an improved convolutional neural network tree species recognition model, can be applied to tree species identification and provides a new approach for forestry tree species identification.