Aiming at the problems of some chicks’ unobvious cloacal features and the influence of light on the collection of chicks’ cloacal images, a method of automatic recognition of chick sex based on convolutional neural network (CNN) and image depth features was proposed to effectively distinguish male and female chicks and enhance the benefit of raising chickens. Taking chicks’ cloacal images collected by the method of anal examination as the research object, a CNN was used to establish vector collection libraries, including the indepth features of both chicks’ cloacal images to be identified and chicks’ cloacal images. Similarity comparison was performed between the collection libraries of the indepth features of chicks’ cloacal images to be identified, and chicks’ cloacal images and the comparative results were ranked. Feature fusion was conducted for the indepth features that were ranked top n in the ranking results and were the most similar to chicks’ cloacal images to be identified and the indepth features of chicks’ cloacal images to be identified. The depth characteristics of the clonal cavity of the chick were highlighted, and then identification was carried out via CNN. The experiment results showed that the accuracy on the test dataset reached 97.04%, and in the production environment reached 96.82%. Compared with conventional CNN methods, it improved the recognition rate for identifying male and female chicks’ cloaca.