### Related Work  
Currently, the widely used methodologies to discover spatial-temporal patterns include traditional matrix decomposition methods, such as Singular Value Decomposition (SVD) [10], Empirical Orthogonal Function (EOF) [11], Dynamic Mode Decomposition (DMD) [12], and Principal Component Analysis (PCA) [13; 14]. However, the traditional matrix decomposition method is suitable for situations that do not have massive data and in which the analysis scenario is not complex [15]. In addition, machine learning methods are frequently employed in spatial-temporal data pattern discovery [16]. The clustering algorithm is the most widely used machine learning algorithm, including the spatial-temporal density-based spatial clustering of application with noise (ST-DBSCAN) [17] and the clustering-based approach for discovering interesting places in a single trajectory (CB-SmoT) [18]. But ST-DBSCAN has a density peak problem. After that, [PERSON] et al. proposed spatial-temporal clustering by using a fast search and find of density peaks (ST-CFSFDP) [19]. This method solved the density peak problem and could distinguish and identify clusters at the same location at different times. However, it cannot identify different spatial distributions on the same time cluster. Moreover, the above two methods use density-based clustering algorithms. Their ability to capture spatiotemporal characteristics is not as good as information divergence. And the clustering algorithms (like k-means) currently used cannot connect the dependencies of time and space, resulting in unreasonable results in time or space [20]. Some scholars used remote sensing data of the SST in the Yangtze River Estuary from 1982 to 2017 and a matrix decomposition method to investigate the seasonal and interannual variation characteristics of the SST in the Yangtze Estuary, China [21]. In experiments using EOF to explore the temporal evolution and spatial distribution in the SST in the Yellow Sea and the East China Sea, the results showed that the SST in the Yellow Sea and the East China Sea showed different warming trends, and the spatial pattern was consistent with previous research results [22]. [PERSON] et al. used EOF to examine the spatial and temporal variations of SST in the Yellow Sea over 29 years [23]. It has been determined that the growth rate of the SST in the Yellow Sea's littoral regions is significantly higher than in the deep regions. Vertical stratification of the water column reveals long-term changes that result in varying surface heating. Cyclisation Empirical Orthogonal Function Analysis (CSEOF) is used to explore interannual and decadal variability in terrestrial water storage associated with ENSO [24]. It can be observed that the methods are independently studying changes in time or spatial distribution. Moreover, with the explosive growth in data volume, matrix decomposition methods are gradually revealing their limitations, while machine learning methods are becoming increasingly popular. Machine learning and visual analysis methods have also given a great impetus to the field of spatial-temporal data discovery. [PERSON] et al. utilized the k-means clustering algorithm to identify the most suitable CMIP5 models and to calculate the SST trends for the 21 st century [25]. In 2023, [PERSON] and colleagues proposed a new spatial-temporal clustering algorithm to explore the spatiotemporal patterns of SST [26]. This clustering algorithm considers the spatiotemporal dependencies. However, its network is complex and lacks interpretability. [PERSON] et al. created a visual analysis system for geospatial data based on a Bayesian network, allowing users to interactively investigate anomalous patterns in geographic data [27]. [PERSON] used co-clustering for the first time in 2020 to investigate the temporal and spatial differentiation of spring phenology in China [28]; [PERSON] et al. used a multidimensional clustering technique to demonstrate a spatial and temporal wave map for the detection of ocean energy potential [29].