This paper provides a comprehensive survey of temporal knowledge graph representation learning (TKGRL) and its applications. It begins by introducing the definitions, datasets, and evaluation metrics for TKGRL. The paper then proposes a taxonomy based on the core technologies of TKGRL methods and provides an in-depth analysis of different methods in each category. Finally, it presents various downstream applications related to TKGs, including time-aware knowledge reasoning, entity alignment, and question answering. The paper concludes with a discussion of future research directions in this area. Key contributions include an extensive investigation of TKGRL methods, a detailed analysis of their core technologies, and the introduction of new classification categories. The paper also highlights the latest developments in TKGRL applications and identifies future research directions, focusing on scalability, interpretability, information fusion, and integration with large language models.This paper provides a comprehensive survey of temporal knowledge graph representation learning (TKGRL) and its applications. It begins by introducing the definitions, datasets, and evaluation metrics for TKGRL. The paper then proposes a taxonomy based on the core technologies of TKGRL methods and provides an in-depth analysis of different methods in each category. Finally, it presents various downstream applications related to TKGs, including time-aware knowledge reasoning, entity alignment, and question answering. The paper concludes with a discussion of future research directions in this area. Key contributions include an extensive investigation of TKGRL methods, a detailed analysis of their core technologies, and the introduction of new classification categories. The paper also highlights the latest developments in TKGRL applications and identifies future research directions, focusing on scalability, interpretability, information fusion, and integration with large language models.