The progress of artificial intelligence (AI) technology has significantly impacted economic growth. Research into the correlation between AI technology and total factor productivity (TFP) in enterprises enhances our understanding of how AI fosters economic efficiency and effectiveness. This study, based on 40,960 data points from 4,395 listed Chinese companies between 2003 and 2022, identifies the beneficial impact of AI technologies on corporate TFP, demonstrating that each additional unit of AI technology increases TFP by 0.046 units. The proposed approach using a Random Forest approach further refines these findings. The empirical results of Random Forest model include correlations of variables, trends in TFP over time, and a comparison of actual versus predicted TFP values, offering deeper insights into the factors driving productivity. Further analysis reveals that corporate innovation capabilities mediate this relationship, accounting for 22.176% of the total effect. Additionally, the infusion of youth into the top management team (TMT) positively moderates the impact of AI technology on TFP. The study’s findings provide a comprehensive understanding of the mechanisms through which AI technologies enhance corporate operations and growth. This pioneering study offers valuable insights for policymaking and business management, outlining a robust framework for leveraging AI to improve enterprise productivity.

The intricate relationship between land use and ecological changes is a critical area of eco-environmental research. However, there is a notable lack of quantitative, analytical studies that explore this relationship in depth. This study aims to bridge this gap by applying a contrastive learning approach to a practical land use application, utilizing big data. We analyzed 2,197 publications from the Web of Science Core Collection spanning from 1998 to 2022. Our methodology correlates textual and visual data, enhancing the categorization and thematic analysis of existing literature. The contrastive learning technique uncovers nuanced relationships between textual descriptions and visual data, providing a comprehensive view of research trends. Key findings include an increasing volume of publications, indicating growing interest and potential in this field; predominant focus areas such as the relationship between climate change and land use, dynamic land use modelling, urbanization impacts, land use management, ecosystem services, and biodiversity. Emerging research hotspots identified through contrastive learning include the interactions between urban thermal environments and land use changes, land use effects on biological communities and soil organic matter, and the relationship between ecological conditions and land use in vulnerable regions. This study not only maps the current research landscape but also employs a contrastive learning approach to predict future research directions, thus enriching the methodological framework for analyzing land use applications with big data.