Abstract: 【Purpose/significance】In the era of digital economy, the embedding of data elements further gives rise to enterprise open in⁃ novation, and under the environment of multi-subject interaction and complementarity and multi-factor oscillation and intertwining, the value of heterogeneous resources for innovation realization brought about by the knowledge flow of the enterprise is gradually high⁃ lighted, so it is of great significance to carry out the research on the embeddedness of data elements and the flow of knowledge in order to further sort out the driving factors of the development of innovation.【Method/process】This study empirically analyses the impact mechanism of data factor embedding on corporate knowledge flow by using multiple regression with a sample of Chinese A-share listed companies from 2012 to 2022.【Result/conclusion】Data factor embedding positively promotes enterprise knowledge flow, with absorptive capacity serving as a partial mediating factor. In high-tech enterprises, the impact of data factor embedding on knowledge flow is more pronounced. Regardless of whether enterprises are located in eastern, central, or western regions of China, data factor em⁃ bedding significantly affects knowledge flow【Innovation/limitation】This study combines the BERTopic model with the text analysis method to improve the relevant indexes of data element embedding. Meanwhile, since knowledge flow is a complex and difficult to mea⁃ sure accurately, how to further improve the measurement of knowledge flow is the direction of future research can be improved.