To meet the demand of future information warfare， it is of great significance to build a digital parallel battlefield system for analyzing the actual battlefield system. Therefore， based on the related technologies of parallel system and digital twin， the ACP method of artificial system， computational experiment and parallel execution is used in this study， and the construction idea of digital parallel battlefield system is proposed. The construction process of artificial battlefield is introduced in detail from the aspects of battlefield entity， service， twin data and twins. The parallel execution flow of actual battlefield and artificial battlefield is analyzed. The method proposed in this study can provide the basis for commanders to design the combat plans， make the combat plans， organize the combat cooperation， and command and control the battle situation more quickly and accurately.

The air battlefield situation provides a general description of the actions and states of all participants， while target combat intention recognition serves as a critical foundation for air battlefield posture assessment. To address the challenge of acquiring a large amount of labeled air target battlefield data amidst intense confrontation and rapidly evolving scenarios， a semi-supervised air targets combat intention recognition model is proposed based on metric learning. The model offers a method for uncovering potential patterns from unlabeled samples， thus reducing the reliance on extensive labeled data. In this model， the target time-series data encoder reduces the dimensionality of target data and produces an embedded representation. Based on this， loss values are calculated by measuring the similarity between labeled target sequences and intention types， as well as between labeled and unlabeled target sequences. The experimental results demonstrate that the model achieves combat intention recognition accuracy rates of 86%， 89%， and 91% with labeled sample rates of 30%， 40%， and 50%， respectively.

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.

Threats often have the characteristics of dynamic evolution with the development of bilateral confrontation. Traditional threat assessment methods are more based on static threats and lack of prediction of dynamic threats. To solve this problem， this paper proposes a threat assessment method for aerial targets under confrontational conditions. Setting the red side as the attacking side and the blueside as the defending side. The reinforcement learning model is established with the enemy target as the agent， and its state space， action space， transition function and reward function are designed. The threat assessment model is established， the threat element index is established， and the threat assessment method is designed. The model is trained， and the trained model can predict the enemy aerial target threat according to the confrontation situation. Through test and analysis， the method is more reasonable for threat assessment of enemy aerial targets under confrontation conditions.