Environmental uncertainty poses a significant barrier to collaborative innovation in platform-driven manufacturing enterprises. However, existing studies often overlook the platform’s dynamic coordination role, and the quantification of uncertainty. This paper investigates the collaborative interaction processes and key influencing factors within the internal innovation ecosystem of platform-based manufacturing enterprises under environmental uncertainty using a triangular fuzzy evolutionary game model. Subsequently, we conducted numerical examples and case validation of the model results, which demonstrated that: (1) External environmental uncertainty primarily affects organizational collaborative stability through three pathways: punitive fluctuations, cognitive divergence, and information arbitrage. (2) The difference between collaborative and non-collaborative benefits drives strategic choices.The risk of free-riding and unilateral revenue growth can significantly weaken the willingness to cooperate. (3) Platform resource integration mitigates the impact of uncertainty through a dual mechanism: On one hand, it reduces collaboration costs by leveraging global resources, reusing technologies, and digitizing processes; on the other hand, it enhances incentives through additional benefits such as two-way collaboration compensation, effectively expanding the feasible probability range of the collaboration strategy. (4) The double-edged sword effect of fines is reflected in two aspects: on the one hand, they compel employees to engage in cooperation through coercive constraints; on the other, they diminish the dynamism of innovative groups.This paper for the first time constructs a triangular fuzzy game model involving ‘platform-innovation group-employees’, integrates platform governance variables and realizes the quantification of uncertainty, providing strategic guidance for collaborative innovation within platform-based manufacturing enterprises, thereby fostering the development of their internal innovation ecosystems, and it aligns with the trend of sustainable and people-oriented manufacturing transformation. Furthermore, this study identified specific ways model results improve operational performance, offering practical insights for manufacturing enterprises.
Guo et al. (2026) studied this question.