Abstract: 【Purpose/significance】This article explored the influencing factors of government data collaborative governance, analyzed the correlation among these factors, and interpreted the driving force-dependence mechanism. Scientific references were provided for clarifying the obstacles of multi subject collaborative governance of government data, improving the efficiency of data collaborative gov⁃ ernance, and mining data value.【Method/process】Extracting the influencing factors of government data collaborative governance from domestic and foreign literature, using expert scoring method to select 13 influencing factors, categorizing these factors into four dimen⁃ sions based on information ecology theory, and using interpretive structural modeling method, the hierarchical relationship of the fac⁃ tors is determined. The cross matrix method was used to obtain the quadrant classification positions of these factors, and a driving force-dependence mechanism model was constructed.【Result/conclusion】The collaborative governance of government data is influ⁃ enced by direct, indirect, and fundamental factors, and is distributed in independent groups, spontaneous groups, as well as dependent groups in the "driving force-dependence" quadrant. In the government data collaborative governance driving force-dependence mechanism model, there are mainly three types of chains: ecological element driven chain, continuous dependence chain, and system⁃ atic driving force-dependence chain.【Innovation/limitation】The hierarchical relationship of influencing factors and the underlying driving force-dependence role of government data collaborative governance have been clarified, but further dynamic analysis is still needed to be strengthened as the number of experts and factors is increased.