This paper proposes a fall detection algorithm based on convolutional neural network and XGBoost. The YOLO-v3 algorithm based on the squeeze-and-excitation block is used to detect the human body area of the picture. Then, the human body pose estimation network is used to obtain the human body joints and feature vectors. Finally, we input the feature vectors into the XGBoost for training to determine whether the human body falls. The experimental results show that the proposed fall detection algorithm has a high accuracy of 98.3%.