The marine environment’s complexity poses considerable difficulties for the stability and reliability of communication links. The restricted coverage of onshore base stations in marine areas makes relay technology a critical solution for extending the communication coverage. Here, connectivity analyses help nodes select the optimal forwarding links, reducing transmission failures and improving the network performance. However, the rapid changes in marine wireless channels and the complexity of hydrological conditions make it challenging to acquire precise channel state information (CSI). In particular, dynamic environmental factors like tides, waves, and wind speed lead to substantial variations in the channel parameters over time. In response to these challenges, this paper puts forward a ship-to-shore communication system using relay ships to extend the coverage of terrestrial base stations. A novel channel modeling method is designed to capture the characteristics of marine wireless channels accurately. Additionally, a machine learning (ML)-based approach is introduced to predict the dual-hop link connection probability at future time points by analyzing historical time-series data on oceanic environmental and ship movement parameters. The proposed model consists of a convolutional-layer-based feature extractor and a bidirectional long short-term memory (BiLSTM) estimator. The CNN module extracts effective high-level features from the input data, while the BiLSTM module further explores the dependencies and dynamic patterns along the temporal dimension. The attention mechanism is introduced to distinguish the importance of the information through a weighted approach. The experimental results show that compared to traditional methods and other deep learning approaches, the proposed CNN-BiLSTM-AM model performs better in terms of its prediction accuracy and fitting ability. The model’s mean squared error (MSE) is as low as 0.0126.