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This paper focuses on the development of modern intelligent air combat decision-making technology, and analyzes the elements and characteristics of intelligent air combat scenarios. It introduces the research status and practical application of existing intelligent air combat decision-making methods, including decision-making methods based on game theory, prior data-driven decision-making method, and decision-making methods based on autonomous learning, and especially focuses on deep reinforcement learning intelligent decision-making methods based on value and strategy. Finally, facing to various challenges of future intelligent air combat and the limitations of traditional deep reinforcement learning, the paper gives the future development direction of deep reinforcement learning technology in the field of air combat, which are multi-agent intelligent decision-making technology for cluster warfare, efficient intelligent decision-making technology for wide area space-time, and generalized intelligent decision-making technology for complex scenarios.