Video data of wild animals from infrared cameras always has a large quantity, which takes a lot of work to select and identify. In order to meet the demand of fast automatic identification, this study, using Northeast Tiger and Leopard National Park as an example, is to explore the practicability of using deep learning, convolutional neural networks to automatically identify different animal species, using videos taken by infrared cameras in the wild, under natural conditions. Pictures of each 8 species, captured from the videos from different seasons and of different conditions, consist of the data set. 2074 pictures for train set and 519 for test set. Region of Interest is selected and labeled, the model is YOLO v3 under darknet framework. All pictures are in one data set in the first group of experiment. In the second group, pictures are divided into day(RGB) and night(Grey), and in the third group, divided into day(RGB) and night(Grey) while fine-tuning is used. The mean average precision of our models is from 84.9% to 96.0%, and the models converge. Results show that althoug it is still needed to use better train set to improve the models, using YOLO v3 to identify wild animals automatically is practicable to save manpower and fine-tuning could be an assistance when the train set is small.