Amid the continuous progress in technology, artificial intelligence technologies have been widely applied to the social life. This study develops a system that can identify abnormal behaviors in videos with predictive values. Firstly, we employ a Two-Stream Inflated3D (Two-Stream-I3D) convolutional neural network to extract features from the video. Secondly, we rely on Python to transform the features into those that can be recognized by a deep learning network. Finally, we perform GRNN training for abnormal probability regression. Experimental results show that the system can achieve the average accuracy of nearly 74% for abnormal behavior recognition during the detection of nearly 50 cases.