With the rapid evolution of wireless communication technologies， multiple standards such as 5G， Wi-Fi， and narrowband internet of things（NB-IoT） coexist， which lead to significantly increased complexity in network architectures and channel environments. This makes wireless technology recognition increasingly vital for spectrum management， interference mitigation， and security monitoring. Traditional methods relying on handcrafted features and rule-based approaches exhibit limited adaptability in dynamic and complex scenarios， particularly when facing unknown protocols. Deep learning has substantially improved recognition accuracy and robustness through end-to-end modeling and automatic feature extraction. However， it still encounters bottlenecks in cross-standard generalization， data scarcity， and computational overhead. In recent years， large-scale pre-trained models， represented by large language models and multimodal models， have demonstrated remarkable potential in complex wireless technology recognition owing to their strong generalization， cross-task transfer， and few-shot learning capabilities. This paper provides a systematic review of the development of wireless technology recognition， with a focus on the latest advances driven by large language models. We analyze the challenges related to generalization ability， interpretability， and efficient deployment， explore the opportunities in intelligent spectrum management and security monitoring， and finally present a forward-looking perspective on their potential applications in intelligent network management for 5G/6G.