With the deep integration of digital technologies into industrial activities, high-tech industries have become increasingly dependent on data resources, while the efficiency of high-tech achievement commercialization remains limited.Based on theories of the digital economy and innovation economics, this study analyzes the industrialization process from the perspective of data factor allocation, drawing on high-tech industry operations and science and technology management practices, and constructs an analytical framework of “data factor allocation–technological innovation–industrialization performance.”The results show that rational data factor allocation can reduce innovation uncertainty, alleviate information asymmetry, and improve high-tech industrialization performance by optimizing collaborative structures. Accordingly, policy recommendations are proposed to improve data governance, strengthen cross-actor data collaboration, and enhance industrialization support mechanisms.