In the context of large-scale UWSNs (underwater wireless sensor networks) used for marine environment monitoring, clustering has been recognized as an effective means of improvingnetwork energy management efficiency [5]. The division of UWSNs into different clusters and the use of cluster head (CH) nodes to schedule and transmit for cluster members are employed to reduce the energy consumption of both individual member nodes and the entire network. However, issues such as heavy loads on CH nodes have led to the application of game theory by many experts to the energy consumption optimization of acoustic clustering networks. A non-cooperative game-based UWSNs energy consumption balancing clustering scheme was proposed by [PERSON] et al. [6], which optimizes the selection strategy of CH nodes to achieve a Nash equilibrium in energy consumption optimization and extend the network lifespan. Nevertheless, this scheme did not take into account the assessment of energy levels of ordinary nodes themselves, nor the significant control overhead of CH nodes. The literature [7] has proposed a Centralized Control-Based Clustering Scheme (CCCS) for Energy Efficiency in Underwater Acoustic Sensor Networks. From a global point of view, the scheme establishes a centralized controller in the cluster to optimize the selection of relay nodes. However, the disadvantages of this algorithm and the differences between it and the proposed algorithm are as follows: Firstly, the CCCS will optimize the selection of CHs according to the collective behavior and interests and lack the evaluation of the node's own energy, resulting in the premature death of some nodes. The algorithm proposed in this paper balances the energy consumption between nodes through a non-cooperative game and prolonging the life cycle of nodes. Secondly, the centralized scheme has fewer application scenarios, which also increases the communication and control overhead of sink nodes. The CHs node in this paper only collects data of member nodes in the cluster, thus greatly reducing the control overhead of the cluster network. After collecting data, CHs first carry out data fusion and then forward it to the sink node through multi-hop. Thus, the communication and control overhead of the sink node is also reduced. Moreover, the complex underwater multipath channel conditions are seldom incorporated into the node game competition model in existing clustering game algorithms, which results in the overall benefits of UWSNs being less applicable to actual underwater scenarios. Therefore, a game-theory-based UWSN adaptive clustering algorithm for marine environment monitoring is proposed in this paper. The main contributions of this paper are as follows:  
1. In the environmental monitoring system design presented in this paper, power supply equipment is placed on underwater data vaults located on floating platforms at the surface, thereby providing power for underwater central nodes. This ensures that the energy of the central nodes remains unrestricted, enabling them to obtain information from all underwater nodes within their communication range, which in turn further ensures the clustering monitoring of a wide range of underwater mobile nodes. 2. A non-cooperative game-based adaptive clustering algorithm is proposed for the purpose of underwater mobile node clustering monitoring. In this algorithm, underwater node multipath channel information is incorporated into the game model, The expected return calculated by this game model is the return of users who participate in the game under the influence of the multipath effect, and the total return of all users jointly determines the performance of the clustering algorithm proposed in this paper to enhance the environmental adaptability of users during the game competition process. 3.