In the context of next-generation wireless communications and multi-source heterogeneous network systems, traditional cryptographic mechanisms and security protocols pose significant risks in Internet of things(IoT) environments. There is an urgent demand for more efficient and reliable identity authentication technologies. Radio frequency fingerprinting identification (RFFI), which leverages the inherent signal characteristics of wireless devices, provides a novel approach to addressing device authentication and security challenges. Unlike existing reviews that focus on selected aspects of RFFI from a broad perspective, this paper proposes a systematic and comprehensive framework. It begins by explaining the fundamental principles and characteristics of radio frequency fingerprint (RFF). Then, from the perspectives of statistical features and deep learning (DL)-based features, the paper presents an in-depth review of RFFI classification and identification methods, along with a comparative analysis of the two approaches supported by experimental validation. Finally, several potential research directions in intelligent RFFI are discussed, and future trends of RFF technology are explored, aiming to offer both theoretical insights and practical guidance for ongoing research and real-world applications.