Abstract: In order to accurately identify the suckling behavior of piglets with unclear features and severe clustering phenomenon, a piglet suckling behavior recognition algorithm based on spatiotemporal context information was established in the experiment. Firstly, key point detection technology was used to locate the lactation area of lateral sows, and then a backbone network based on spatial context information module was used to extract the spatial context information of piglets’ suckling status in the lactation area, including the relative position and distance information between piglets and sows, as well as the contour shape features of the connection between piglets’ mouths and sows’ breasts. To capture the motion characteristics of piglet suckling behavior, a temporal context information module was introduced in the last layer of the backbone network to extract the temporal features of piglet suckling behavior between adjacent frames. Finally, the features were put into the long short term memory（LSTM） network for predicting and recognizing piglet suckling behavior. Using accuracy, recall, and average accuracy as evaluation indicators, the performance of the piglet suckling behavior recognition method based on spatiotemporal context information was compared with the Darknet53 algorithm and the original YOLOv5 backbone network+LSTM algorithm. The results showed that the accuracy, recall, and average accuracy of the piglet suckling behavior recognition algorithm based on spatiotemporal context information for piglet lactation behavior recognition were 96.9%, 96.1%, and 96.3%, respectively, which were 14.7%, 14.5%, and 14.4% higher than the Darknet53 algorithm. Compared with the original YOLOv5 backbone network and LSTM algorithm, the results were improved by 12.5%, 11.0%, and 11.3%, respectively, indicating that the piglet suckling behavior recognition algorithm based on spatiotemporal context information had good recognition effects on piglet suckling behavior.