Abstract: Modern warfare encompasses numerous domains and is characterized by its extensibility, variability, interdisciplinarity, and stereoscopic. The vast amounts of information significantly increase the complexity for commanders to grasp the overall battlefield situation and make scientific and accurate decisions. The needs of military intelligent decision-making is closely adhered. The prediction problem of task completion rates at various levels of the battlefield is focused on. A hybrid approach that integrates qualitative and quantitative methods is utilized, large model knowledge, expert experience, and real-time battlefield situational information are based on, a prediction method of task completion rates based on Bayesian estimation is proposed. Verified through simulation and deduction platforms, this method identifies critical change points in the battlefield situation in time by identifying anomalous variations in predicted completion rates, thus providing intelligent support for commanders to make scientific decisions.