A method for recognizing a body movement, the method comprising: sampling an input to-be-recognized video to obtain a sampled image frame sequence of the to-be-recognized video; performing key point detection on the sampled image frame sequence by using a trained body key point detection model, to obtain a body key point position heat map of each sampled image frame in the sampled image frame sequence, the body key point position heat map being used to represent a probability feature of a position of a preset body key point; and inputting body key point position heat maps of the sampled image frame sequence into a trained movement classification model to perform classification, to obtain a body movement recognition result corresponding to the to-be-recognized video; wherein the method further comprises: training to obtain the body key point detection model based on a labeled sample image set, comprising: acquiring a sample image set, wherein the sample image set comprises a sample image and labeling information of a body key point position in the sample image; generating , based on a labeled body key point position in the sample image, a probability heat map with a probability decreasing progressively from the body key point position as a center toward peripheries of the body key point position, the probability heat map being a labeling result of a body key point position heat map of the sample image; and inputting the sample image into a neural network corresponding to a to-be-trained body key point detection model, iteratively adjusting parameters of the neural network corresponding to the to-be-trained body key point detection model based on a difference between a prediction result of the neural network corresponding to the to-be-trained body key point detection model and the labeling result of the body key point position heat map of the sample image, and stopping iteration in response to a preset first convergence condition being satisfied, to obtain the trained body key point detection model. sampling an input to-be-recognized video to obtain a sampled image frame sequence of the to-be-recognized video; performing key point detection on the sampled image frame sequence by using a trained body key point detection model, to obtain a body key point position heat map of each sampled image frame in the sampled image frame sequence, the body key point position heat map being used to represent a probability feature of a position of a preset body key point; and inputting body key point position heat maps of the sampled image frame sequence into a trained movement classification model to perform classification, to obtain a body movement recognition result corresponding to the to-be-recognized video; acquiring a sample image set, wherein the sample image set comprises a sample image and labeling information of a body key point position in the sample image; generating , based on a labeled body key point position in the sample image, a probability heat map with a probability decreasing progressively from the body key point position as a center toward peripheries of the body key point position, the probability heat map being a labeling result of a body key point position heat map of the sample image; and inputting the sample image into a neural network corresponding to a to-be-trained body key point detection model, iteratively adjusting parameters of the neural network corresponding to the to-be-trained body key point detection model based on a difference between a prediction result of the neural network corresponding to the to-be-trained body key point detection model and the labeling result of the body key point position heat map of the sample image, and stopping iteration in response to a preset first convergence condition being satisfied, to obtain the trained body key point detection model. <EOS>