Significant advancements in Knowledge Graph Question Answering （KGQA） technology have been driven with the rapid evolution of artificial intelligence technologies and deepening research and application of Knowledge Graph （KG）. Owing to lacking systematic classification framework for the existing question-answering methods， current users have limited awareness of various KGQA models. Therefore， it is difficult to meet the reference needs of users across different domains. To address this issue， a systematic review of research progress in KGQA domain over past 15 years was conducted. First， four core question-answering strategies were identified including template matching， semantic parsing， deep learning and Large Language Model （LLM） augmentation. Second， the performance of eight typical question-answering methods under four categories of strategies under complex question， multi-hop reasoning， low-resource environments， and multi-lingual environments was compared， and applicable scenarios for these methods were provided. Third， common datasets and performance evaluation methods were organized. Finally， combining the current state of domain development， targeted suggestions and prospects for future research directions in KGQA technology were proposed， aiming to provide references for subsequent related studies and applications.