Abstract: Analyzing the connotation, characteristics and logical framework of enterprise high-quality dataset construction is essential to unlocking data value and enabling firms' digital-intelligent transformation. This paper conceptualises enterprise high-quality datasets, identifies six core attributes, establishes a three-dimensional framework (value, scenario, technology) based on socio-technical systems theory to provide theoretical support for the construction of high-quality corporate datasets under the "AI Plus" background; subsequently, from the analytical perspectives of the data value chain and data life cycle theories, it constructs a five-dimensional model encompassing scenario demand perception, data resource weaving, knowledge resource extraction, data-knowledge integration and refinement, and a digital-intelligence service ecosystem; finally, in response to the practical dilemmas currently faced by enterprises, the study proposes targeted optimization strategies, suggesting that future efforts should focus synergistically on data scenario shaping, data aggregation and governance, data labeling optimization, and data security assurance to overcome the bottlenecks of high-quality dataset construction, enhance the value-enabling efficiency of data elements, and provide robust support for the digital-intelligence transformation and high-quality development of enterprises.

Key words: Artificial intelligence plus, Enterprise data, High-quality datasets, Data elements, Data governance