Abstract: As a representative technology of deception defense, honeypot can significantly change the dynamic potential of the defender, but with the development of the attacker’s technology, the current honeypot is easy to be bypassed by the attacker after being identified due to its preset configuration and fixed deployment. Therefore, it is more and more important to study the adaptability enhancement of honeypot design and deployment. Starting from the two most widely used adaptive enhancement methods-game theory and reinforcement learning, this paper summarizes their applications in the design and deployment of adaptive honeypots, compares and analyzes the advantages and disadvantages of the current research, and finally briefly looks forward to the improvement direction of the two methods in the enhancement of honeypot adaptability.