The rapid proliferation of marine economic activities has led to explosive growth in maritime communication demands. Consequently, the scarcity of spectrum resources, the highly dynamic spectrum environment, and diverse service requirements have become increasingly critical issues. While conventional deep reinforcement learning (DRL) methods perform well for specific training scenarios, they fail to generalize effectively to unknown environments. To address this challenge, this paper investigates a maritime dynamic spectrum access and service scheduling scheme based on adaptive meta-reinforcement learning. First, we construct a channel occupancy model that encompasses both Markov frequency-hopping mode and spread-spectrum frequency-hopping mode. By incorporating a dual-queue mechanism for urgent and normal data packets, we formulate the multi-agent cooperative decision-making problem as a decentralized partially observable Markov decision process (POMDP). Second, to overcome the limited generalization capability of traditional DRL algorithms, we propose a spectrum access and service scheduling algorithm based on a meta-learning paradigm (MetaSASS), which facilitates rapid policy transfer through meta-training across a distribution of tasks. Furthermore, to resolve the adaptation efficiency bottleneck caused by fixed inner-loop learning rates, we design an adaptive meta-reinforcement learning SASS algorithm (AMRLSASS). This algorithm employs a task encoding module to dynamically adjust learning rates, thereby balancing convergence speeds across tasks of varying complexities. Simulation results demonstrate that the proposed AMRLSASS algorithm outperforms baseline algorithms across a series of metrics within unknown environments and effectively validate the superiority of the proposed method in complex maritime environments. The algorithm proposed in this paper provides an effective solution for the rapid adaptation of intelligent spectrum access and service scheduling to unknown scenarios in maritime wireless communication systems.
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