Currently, millimeter-wave imaging system plays a central role in security detection systems. Existing concealed object detectors for millimeter-wave images can only detect pre-trained categories and fail when encountering new, unseen categories. Accurately identifying the increasingly diverse types and shapes of concealed objects is a pressing challenge. Therefore, this paper proposes a novel open vocabulary detection algorithm: Open-MMW, capable of recognizing more diverse and untrained objects. This is the first time that open vocabulary detection has been introduced into the task of millimeter-wave image detection. We improved the YOLO-World detector framework by designing Multi-Scale Convolution and Task-Integrated Block to optimize feature extraction and detection accuracy. Additionally, the Text-Image Interaction Module leverages attention mechanisms to address the challenge of feature alignment between millimeter-wave images and text. Extensive experiments conducted on public and private datasets demonstrate the effectiveness of Open-MMW. Compared to the baseline model, Open-MMW improves recall by 13.7%, precision by 13.9%, mAP@0.5 by 14.2%, and mAP@[0.5–0.95] by 10.3%.The performance improvements are even more significant compared to state-of-the-art multimodal interaction models, showcasing powerful zero-shot detection capabilities not present in traditional closed-set detection.