With the continuous evolution of war forms and the upgrading of weapons and equipment， the air battlefield situation is becoming more and more complex. Quickly and accurately identifying the combat intention of the target is an important content of battlefield situation assessment， which can provide auxiliary information for commanders to make decisions and help them to seize the initiative in the war. This paper firstly introduces the basic concepts and related models of target intention recognition， defines the concepts of target intention and intention recognition， and determines the status and importance of target intention recognition from two aspects of the operational command decision-making process and information fusion process. Secondly， target features and intention space， as input attributes and recognition framework for intention recognition respectively， are the basis of intention recognition and are reviewed. Then， five common target intention recognition methods such as rule and template matching， evidence theory， Bayesian network， traditional machine learning and neural network are reviewed. The basic mechanism and recognition process of each recognition method are introduced， and its advantages and disadvantages are summarized. Finally， the performance of five kinds of target combat intention recognition methods is compared and analyzed， and the future research direction is prospected.