In response to the problem of the global value chain (GVC) reconstruction path of emerging economy enterprises, this study is based on the perspective of new structural economics, revealing the internal mechanism and transmission path of data driven global value chain reconstruction. The aim is to crack the synergy between digital elements and industrial structure and division of labor structure, and promote the dual reconstruction of GVC in terms of division of labor form and spatial layout. This study constructed a dual model empirical framework consisting of a benchmark nonlinear driving model and a structural adaptation adjustment model. Case data from 66 Chinese manufacturing enterprises were selected to conduct quantitative tests on variables such as digital factor input, GVC production length, geographic concentration, and structural adaptation. The driving mechanism was validated through component regression and interaction effects. Case analysis shows that digital factor input has a significant inverted U-shaped direct driving effect on GVC reconstruction, with positive moderating effects on demand income elasticity, industry correlation, industry scale, and global regional value chain embedding level. High digital factor input enterprises have significantly better GVC extension and spatial agglomeration performance than low input enterprises. Research has shown that the coupling of data-driven and structural adaptation is the core mechanism of GVC reconstruction, and enterprises should enhance their structural adaptation capabilities to achieve efficient value chain upgrading.
