## 1 Introduction  
An opportunistic network mainly consists of nodes with limited energy; such nodes have various kinds of network cards such as WiFi, Bluetooth, 4G, 5G, satellite, etc., which come with functions such as data collection, computation, and transmission [1]. Any facility that carries a communication network can be a node, and, in maritime opportunistic networks, communication relies mainly on ships or fixed base stations at sea, such as buoys and platforms, which become nodes, as previously described. In opportunistic networks, the transmission of messages from the source node to the destination node also requires the assistance of relay nodes to store and carry the messages [2]; the nodes utilize the multi-hop mode to transmit all the data to the relay node, which fuses all the data to the network base station and, ultimately, sends the data required by the user to the user [3]. Due to the intermittent communication between nodes, the resources around the relay nodes are dynamic, changing, and uncertain [4]. Also, the maritime environment is different from the land, the meteorological conditions are complex and changeable, and the network users are highly mobile and sparsely distributed. The design of the maritime opportunistic network routing should contain a reasonable selection of communication resources to carry out an effective evaluation of the relay nodes [5] and to determine the forwarding priority of all the nodes in the candidate relay set, to avoid or inhibit the duplication of transmission [6]. In the current research that has bee nproposed for solving routing schemes for opportunistic networks, most works are based on replicating data copies for wide dissemination in order to improve the packet transmission performance [7], but this can lead to the wastage of network resources or even network congestion. There are also schemes that utilize link prediction to transform link availability between pairs of nodes into a probabilistic representation as a metric for routing decision making [8]. But the link prediction in which the sequence learner employed is concerned with learning the correlation in time series, while, in opportunistic networks with intermittent communication, more attention should be paid to link connectivity to cope with the highly variable link performance and improve the communication period. In [9], an opportunistic network routing scheme based on network connectivity assessment is proposed, where a breadth-first algorithm constructs a connectivity graph between a source node and a target node to determine the two nodes' connectivity. But this algorithm has high space complexity, consumes a large amount of memory, and is not suitable for dealing with large-scale data. In a weighted graph, there is no guarantee that the path with the smallest weight can be found. This research focuses on addressing the above limitations. The proposed deep learning-based link connectivity assessment for opportunistic network routing strategies firstly performs link prediction for link connectivity between pairs of nodes, takes into account the further forwarding state of the nodes in the future moments, and, on this basis, determines the forwarding priority of the nodes based on their comprehensive attributes in the set of candidate relays, then designs the routing scheme. The 6G networks will incorporate AI-driven dynamic spectrum access and spectrum agility mechanisms, where links adapt dynamically based on real-time spectrum conditions, interference levels, and coexistence requirements [10]. The link connectivity prediction model in this paper could be extended to predict spectrum availability, helping 6G networks to optimize routing based on real-time spectrum usage. Hence, deep learning-based link prediction will be crucial in 6G's ultra-dynamic, software-defined network environments.