Artificial intelligence technology and image recognition technology are playing an increasingly important role in information warfare, while battlefield image recognition and information processing are at the heart of information processing in warfare. This research will use deep learning image recognition technology and QT development platform, combined with target damage tree analysis and Bayesian network inference method, to research and develop the design of large-scale surface warships damage assessment system. A large-scale surface warships damage assessment system was designed. The system can quickly identify the target large-scale surface warships type with an accuracy rate of over 91%. On this basis, damage assessment is carried out in terms of target vulnerability, combatant power analysis, and bullet-eye rendezvous. A new damage classification is established. The system can improve the efficiency of large-scale surface warships damage assessment, can be well combined with the front-line information collection pictures to assess, and overcome the traditional large-scale surface warships damage assessment and problems of slow and inaccurate manual processing of raw data. It provides a new way of thinking for large-scale surface warships damage assessment research.

A micro-unmanned aerial vehicle (UAV) swarm has high flexibility and intelligent unmanned flight and stealth capabilities. Moreover, it can attack both single and group targets, which will occupy dominant positions in the future information war. To satisfy the new micro-UAV swarm information combat mode and contribute to the advances in ammunition flexibility, efficiency, miniaturization, and multipurpose applicability, a 10 mm caliber micro-shaped charge warhead was designed in this study. The results indicate that the micro-shaped charge can be assembled with a micro-UAV swarm to efficiently damage the targets without relying on the weapon launch velocity. To realize a lightweight and high-efficiency micro-shaped charge structure, the formability of Teflon, nylon, polycarbonate (polycarb), and metallic copper liners was studied using the finite element analysis. The penetration efficiencies of different micro-jets on gelatin targets were compared and analyzed. It was found that the micro-shaped charge nylon and Teflon jets could pass through the gelatin target and transfer more energy to it compared with copper. The observed penetration aperture was also larger. Although the penetration depth of the polycarb jet to the target was the smallest, the penetration aperture was the largest. The findings of this study can serve as a basis for the development of micro-shaped charge technology.