Spatial Distribution Characteristics of Marine Economy Based on AI-Assisted Multi-Source Data Fusion and Random Forest Analysis

Understanding the spatial dynamics of China’s marine economic geography is essential for sustainable coastal development and marine spatial governance. This study examines the spatial distribution patterns and influencing factors of spatial differentiation in China’s marine economy from 2013 to 2023, utilizing AI techniques to facilitate multi-source data fusion and employing a Random Forest analytical method. The research was integrated with AI-based web-scraping, automated data-cleaning procedures, multi-source data preprocessing, Min–Max normalization, and Random Forest regression to accomplish multi-source data fusion and factor-importance analysis. Kernel density estimation, global Moran’s I, Getis-Ord Gi* statistics, and buffer zone analysis were employed to characterize spatial heterogeneity across coastal, island, and maritime economic zones, while Spearman’s correlation was used to quantify the relationships of influencing factors. Results indicate that China’s marine economy exhibits a pronounced “south–hot–north–cold and east–strong–west–weak” spatial gradient, with high-value clusters concentrated in the Bohai Rim, Yangtze River Delta, and Guangdong–Hong Kong–Macao Greater Bay Area. The coastal zone economy accounts for over 65% of the national marine GDP and acts as the dominant driver of spatial agglomeration. Policy implications suggest strengthening cross-regional industrial cooperation and optimizing spatial planning to enhance marine economic resilience and sustainability.