A Machine Learning Based Method for Situational Target Threat Assessment

With the rapid development of artificial intelligence technology, its application in the military field is becoming increasingly widespread, especially in battlefield situational awareness and threat assessment, demonstrating enormous potential. This article proposes a machine learning based method for assessing the threat of naval battlefield situational targets, establishes a model that integrates various target parameters, and uses this model to train on manually annotated data to obtain ideal weight parameters. After verification, the training effect is relatively ideal and has great practical value.