To address the challenge that existing abnormal behaviors detection methods are difficult to balance efficiency and effectiveness, we proposed an efficient hybrid algorithms framework combining YOLO (You Only Look Once) and GCN (Graph Convolutional networks). The framework consists of two stages: detection and recognition. In the detection stage, an object detection algorithm and MOT (a multi-object tracking) algorithm are employed to initially detect abnormal actions in frames captured by video sensors. In the recognition stage, a pose estimation algorithm is utilized to extract the skeleton data information from suspicious video clips as the input to a graph convolutional network, which then further determines whether there are abnormal behaviors in the video clips. Experimental results show that the proposed framework achieves an average recognition accuracy of 95.87% and a recognition speed of 41 fps on a self-built dataset consisting of five types of behaviors, demonstrating its effectiveness in real-time detection and classification tasks for security.