## 4 Discussion  
This paper studies the spatial-temporal distribution of the SST in the SCS using the proposed algorithm. By analyzing the SST in the SCS at different periods for experimental comparison, we make the following findings:  
1. Through the spatial-temporal co-clustering results, it can be determined that the SST distribution in the SCS and its coastal waters changes greatly with time. Especially in winter, the SST distribution is relatively uniform, and the difference in SST across the entire sea area is not significant. In summer, due to the influence of circulation and monsoons, the average SST of the entire sea area is higher, and the difference between the north and the south is also large. The two seasons, spring, and autumn, have the same spatial distribution. In years when the ENSO phenomenon occurs, the SST will be significantly lower or higher than in normal years, but this will not affect the overall SST spatial distribution in the SCS. 2. By observing the cluster boundaries, the class boundaries are usually in the northeast-southwest direction. And the closer it is to the continent, the more inclined the class dividing line is, which can indicate that the SST distribution in the SCS and its adjacent seas can also be found to be affected by the land. Because the heat capacities of land and ocean are different, the SST near land is more susceptible to regulation by the land. 3. Based on the SST clustering results of the SCS when the ENSO phenomenon occurs, we determined that when the ENSO phenomenon occurs, it will have an impact on the time clustering results of the SST in the SCS. Specifically, the impact of ENSO events on the SST in the SCS will affect the SST in the SCS again after a period. And it is related to the intensity of ENSO events. The greater the intensity, the shorter the lag time. Studying the spatial-temporal patterns of the SST in the SCS has the following significance:  
1. By studying the spatial-temporal patterns of SST in the SCS, we can encode the discovered patterns into deep learning input to improve the accuracy and interpretability of the algorithm. This helps us more accurately model and predict regional and even global climate change. Subsequently, we will encode the spatial-temporal distribu \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{4}{*}{La Niña event} & SST correlation between & SST correlation between \\  & 1998.11–1998.12 and & 2010.11–2010.12 and \\  & 1999.05–1999.06 & 2011.05–2011.06 \\  & (Intense La Niña event) & (Mild La Niña event) \\ \cline{2-3}  & 83.5\% & 68.2\% \\ \hline \multirow{4}{*}{El Niño event} & SST correlation between & SST correlation between \\  & 1997.06–1997.07 and & 2014.11–2014.12 and \\  & 1999.12–1998.01 & 2015.05–2015.06 \\  & (Intense El Niño event) & (Mild El Niño event) \\ \cline{2-3}  & 80.1\% & 63.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Verification of SCS’s SST lagged correlation during the selected ENSO periods. It demonstrates the lagged correlation of SST time series in the abnormal time category under ENSO events of different intensities. tion identified in this article into the feature input of the deep learning algorithm to improve the accuracy of SST prediction. 2. The SCS is often affected by extreme weather such as typhoons [43], and SST is one of the important factors affecting the intensity of typhoons [44]. Therefore, studying the spatial-temporal patterns of the SST in the SCS can improve our ability to predict and respond to these disasters. 3. Regarding marine ecological protection, SST has a direct impact on the health of marine ecosystems, especially sensitive ecosystems such as coral reefs. Studying spatial-temporal patterns can help us understand and protect these ecosystems [45]. Marine heatwaves are extreme ocean phenomena often caused by persistent anomalies in SST. We are also conducting research on marine heat waves. Through the SST spatiotemporal pattern we discovered, we can predict marine heat waves more accurately.