Recognizing human behavior in images is a challenging and important task. As the image recognition of human behavior recognition has gradually begun to be used in daily life, such as automatic monitoring system in the detection of abnormal events, sports analysis and film classification. This research will optimize and improve the 3D ResNet-18 based model to propose a simple and less hyperparameter adjustment modular architecture. The experimental results on the KTH and UCF-101 data sets show that the improved algorithm accuracy (Top-1) is 96.3% and 60.01%, and the improved model can take more effective features and improve the recognition effect of human behaviors compared with the original 3D ResNet-18 Model.