###### Abstract  
Reconstructing the three-dimensional thermal structure of the ocean under typhoon conditions presents significant challenges due to the scarcity of observational data, particularly in subsurface regions, and the limitations of existing observation methods in terms of spatial and temporal resolution. Accurate reconstruction of these structures is crucial for understanding the energy exchange between the ocean and typhoons, as this exchange directly influences typhoon intensity and trajectory. To address these challenges, this study introduces a fully connected transformer network (FCT), which integrates fully connected layers with a transformer model. The FCT model leverages the attention mechanisms inherent in the transformer architecture to effectively extract and integrate multi-scale ocean dynamical features. Using data from Typhoon Lekina in 2019, this study reconstructs ocean thermal structures at various depths and achieves an RMSE of 1.03 \({}^{\circ}\)C and an MAE of 0.83 \({}^{\circ}\)C when validated against Argo data. Furthermore, the model's robustness was demonstrated through five-fold cross-validation, with the validation loss exhibiting minor fluctuations across folds but remaining stable overall, with an average validation loss of 0.986 \({}^{\circ}\)C, indicating the model's generalizability. Sensitivity analysis also revealed the model's resilience to variations in key input variables, showing minimal impact on output even with perturbations of up to 10% in input data. In addition, the study incorporates content analysis of typhoon-related news reports from 2011 to 2020, revealing a predominance of political topics, which underscores the central role of government in disaster response, with economic and ecological topics following. This integrated approach not only enhances our understanding of the interactions between ocean thermal structures and typhoon dynamics but also provides valuable insights into the societal impacts of typhoons, as reflected in media coverage, contributing to improved disaster management strategies. type: 3 September 2024  
22 October 2024  
2024 power [4]. For example, Hurricane Katrina in 2005 caused severe damage to the New Orleans area in the United States, demonstrating the destructive power of typhoons [5]. When a typhoon passes, it causes significant disturbance to the upper ocean. The upper ocean responds to the typhoon, resulting in changes in temperature, salinity, and the mixed layer, while the ocean also provides energy for the development of the typhoon [6]. Therefore, the thermal structure of the ocean is an important factor affecting typhoons [7]. Studies have shown that warm sea surface temperatures can significantly enhance the intensity of typhoons, causing them to rapidly upgrade to strong typhoons in a short time [8]. For example, Typhoon Pamela in 2015 rapidly intensified over a warm sea surface [9]. Accurately observing typhoon intensity and achieving accurate predictions of typhoon development trends are crucial for disaster prevention and social and economic progress, attracting widespread attention from researchers worldwide [10]. The impact of typhoons on coastal areas is particularly significant. Strong winds and heavy rains can trigger storm surges, floods, and landslides, causing great damage to coastal infrastructure, agricultural production, and residents' lives [11]. Therefore, understanding and predicting the impact of typhoons on the thermal structure of the ocean is crucial for reducing disaster risks in coastal areas [12]. In recent years, deep learning techniques, such as convolutional neural networks (CNNs) and transformer models, have been increasingly applied to the study of typhoon dynamics and ocean thermal structures. For instance, studies have shown that CNNs can effectively extract complex spatiotemporal features from satellite data [13].