An encirclement decision-making method based on adaptive curriculum learning and multi-agent proximal policy optimization (MAPPO) algorithm was proposed to address the problem of encirclement and game confrontation for unmanned surface vehicle (USV) swarms in complex marine environments. For maritime encirclement and defense tasks, a combat simulation environment including dynamic targets and multiple reefs was constructed, and the judgement criteria for successful encirclement and task termination were defined. To improve the decision-making efficiency in complex adversarial environments, a normalized state space representing the motion states of both friendly and hostile parties, multi-scale reward functions, and a continuous action space were designed. An adaptive curriculum scheduler was introduced into the centralized training and decentralized execution framework to dynamically adjust the environmental complexity and the noise intensity of action exploration. The results of multiple sets of adversarial simulation experiments indicate that, compared to the baseline methods (i.e., no curriculum learning and traditional curriculum learning methods), the proposed method effectively improves the encirclement success rate, shortens the average task completion time and the average encirclement path length, reduces the number of collisions, and exhibits good generalization ability and adversarial adaptability.