In order to improve the accuracy of stunning state recognition of broiler chickens, a method of stunning state classification of broilers based on regional convolutional neural network (RCNN) was proposed. The following method was able to detect insufficiently appropriately and excessively stunned conditions of broilers. Initially, the image acquisition platform was utilized to collect the sample images. The data sets of collected samples were made according to the PASCAL visual object classes data set format. The total samples of 2319 images were randomly divided into training set and test set with the ratio of 6∶3. The augmented training sets were obtained through image enhancement technology. A Faster-〖JP〗RCNN was trained by using the augmented training set to detect the stunning states of broilers. The results showed that the recognition accuracy of the Faster-RCNN was 96.51% for 773 sample images in the test set. The accuracy of Faster-RCNN model was significantly higher than that of the established back propagation neural network (BP-NN) model (90.11%). The proposed model could be used to inspect the stunning state of more than 37000 broilers per hour. Deep learning technology was applied to recognize the stunning states of broilers, which can be used to automatically detect the stunning state of broilers and enhance automated slaughtering processes in the poultry industry. 