With the intensification of conflicts in different regions, the W-band suspicious object detection system is an essential security means to prevent terrorist attacks and is widely used in many crucial places such as airports. Because artificial intelligence can perform highly reliable and accurate services in the field of image recognition, it is used in suspicious object detection systems to increase the recognition rate for suspicious objects. However, it is challenging to establish a complete suspicious object database, and obtaining sufficient millimeter-wave images of suspicious objects from experiments for AI training is not realistic. To address this issue, this paper verifies the feasibility to generate a large number of millimeter-wave images for AI training by generative adversarial networks. Moreover, we also evaluate the factors that affect the AI recognition rate when the original images used for CNN training are insufficient and how to increase the service quality of AI-based W-band suspicious object detection systems for moving persons. In parallel, all the international standardization organizations have been collectively advancing the novel technologies of AI. We update the reader with information about AI research and standardization related activities in this paper.