Abstract: With the wave of the past decade, the development of artificial intelligence is in the critical period from perceptual intelligence to cognitive intelligence. Knowledge graph, as the core technique of knowledge engineering in the era of big data, is the combination of symbolism and connectionism, and is the cornerstone of realizing cognitive intelligence. It provides an effective solution for the knowledge organization and intelligent application in the Internet era. In recent years, some progress has been made in the key technologies and theories of knowledge graph, and typical applications of knowledge graph based on information system have gradually entered various industries, including intelligent question answering, recommendation system, personal assistant, etc. However, in the context of big data environment and new infrastructure of China, the increasing multi-modal data and new interaction ways have raised new demands and brought new challenges to the new generation of knowledge graph in terms of basic theory, architecture, and key technologies. We summarize the research and development status of key technologies of the new generation knowledge graph at home and abroad, including unstructured multi-modal data organization and understanding, large-scale dynamic knowledge graph representation learning and pre-training models, and neural-symbolic knowledge inference. We summarize, compare and analyze the latest research progress. Finally, the future technical challenges and research directions are prospected.