This study investigates the evolution and prospective directions of big data applications within the global digital economy over the past twelve years. A comprehensive bibliometric analysis was conducted using Biblioshiny and included 752 documents authored by 1748 scholars and published in 416 specialized journals and academic books between 2013 and 2024. The findings reveal that scholarly interest in this area peaked in 2024. Co-occurrence network mapping highlights three dominant thematic trends in the applicability of big data within the digital economy: technological innovations, conceptual frameworks, and the role of China. Influential academic publications—such as Sustainability, PLoS ONE, and the proceedings of the 8th International Conference on Information Technology and Quantitative Management—have played a pivotal role in advancing research in this domain. Moreover, leading institutions, including the University of the Chinese Academy of Sciences, Shenzhen University, and Guizhou University, have emerged as pivotal contributors to advancing research in this field. China is the primary driving force and key player in reshaping the digital economy through big data, a role that is expected to contribute to global technological advancement in the future.

This study develops a unified conceptual framework that explains the interdependencies among Data Mining (DM), Big Data Analytics (BDA), and Database Systems (DBS) within modern data architectures. To achieve this objective, the research introduces an extended SLR methodology that triangulates the PRISMA protocol, bibliometric analysis, and topic modeling, enabling the identification of technological clusters, thematic structures, and the evolutionary trajectories of the field. The findings show that the Big Data ecosystem is organized around a conceptual axis of “infrastructure–application–knowledge,” in which distributed infrastructures and NoSQL technologies constitute the architectural foundation, applied analytics links technological capabilities to organizational use cases, and knowledge extraction from unstructured data marks the semantic and cognitive maturation of the domain. Co-citation, co-occurrence, MCA, and thematic-map analyses confirm the existence of distinct yet complementary epistemic schools, while topic modeling highlights the convergence of Big Data architectures, advanced analytics, and operational governance. The study thus contributes to consolidating an integrated perspective on DM–BDA–DBS and provides a replicable methodological framework for exploring conceptual evolution in the Big Data domain.