Unmanned Aerial Vehicles (UAVs) are widely used as communication relays for ground search units in complex and remote areas. However, the avoidance of risky areas, such as no-fly zones, and the evasion of radar interception remain critical challenges. A Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3)-based dynamic UAV deployment algorithm for multi-UAV-assisted communication coverage with low interception risk (MATD3-RC2) is proposed. A centralized training and decentralized execution framework is adopted, eliminating the need for global environmental awareness. The communication coverage problem is formulated as a multi-objective optimization task, aiming to maximize coverage effectiveness, minimize radar interception risk, and ensure robust UAV connectivity. The simulation results demonstrate that MATD3-RC2 outperforms both the Multi-Agent Deep Reinforcement Learning-based energy-efficient control (MADRL-E) and the Distributed Virtual Force Motion Control (DVFMC) algorithms in terms of the target performance metrics.