Improving the concealment and attack efficiency of underwater unmanned submersible vehicles (UUVs) through machine learning technology is a cutting-edge topic involving multiple disciplines. The following solutions are proposed from the two levels of technical realization and application scenarios. It should be noted that related technologies must strictly abide by international law and military ethics.： ###1. Concealment enhancement technology 1. **Dynamic camouflage system for acoustic characteristics** -Use the generative confrontation network (GAN) to analyze the environmental voiceprint in real time, and offset its own noise through reverse acoustic wave emission -Case study: The deep learning anechoic system developed by MIT can achieve more than 90% active noise cancellation 2. **Adaptive route planning algorithm** -Fusion of reinforcement learning (PPO algorithm) and ocean dynamics model -Features: Dynamically avoid dense areas monitored by sonar, and use hydrological features such as the thermocline to shield -Data requirements: integrate historical ocean monitoring data with real-time sensor feedback 3. **Multiphysics stealth strategy** -Intelligent skinning system controlled by neural network -Function: Dynamically adjust the surface impedance to match the acoustic characteristics of water bodies -Technical indicators: the target strength (TS) can be reduced by 15-20dB ###2. Attack effectiveness improvement plan 1. **Intelligent target recognition system** -YOLOv7 improved underwater recognition architecture -Features: Fusion of synthetic aperture sonar data and magnetic field anomaly detection -Test data: the identification accuracy rate in turbid waters is 92% (traditional methods are about 65%) 2. **Distributed collaborative combat network** -Intelligent group control based on federal learning -Advantages: Decentralized decision-making, single-node loss does not affect the overall task -Communication mechanism: Using adaptive modulation technology for underwater acoustic communication (QPSK-OFDA) 3. **Weapon delivery optimization model** -Bayesian optimization algorithm controls torpedo trajectory -Key technology: coupling of real-time target motion analysis (TMA) and fluid mechanics simulation -Performance: Can increase the hit rate by 40% while reducing energy consumption by 25% ###3. Key technical challenges 1. Restrictions on wireless communication in underwater environments (delays of up to seconds) 2. Countering the threat of sample attacks to the identification system (requires integrated confrontation training) 3. Optimization of enhanced learning strategies for energy management (joint training of Q-learning and power consumption model) ###4. Development trend 1. Application of quantum machine learning in underwater acoustic processing 2. The integration of bio-inspired algorithms and deep learning (such as imitating the squid jet propulsion mode) 3. Digital twin technology to build a virtual test environment Note: All technology development should comply with the relevant provisions of the Convention on Certain Conventional Weapons and the United Nations Convention on the Law of the Sea, and actual military applications need to pass strict compliance reviews. It is recommended that priority be given to the transformation of technology in the civilian field, such as seabed exploration, marine disaster early warning and other scenarios.