Simulation study of enterprise intelligent transformation behavior based on complex network evolutionary game

The accelerating global economic competition and the rapid development of intelligent technologies present both new opportunities and challenges for enterprises. Intelligent transformation has become an imperative trend for enhancing competitiveness, yet Chinese enterprises are still in the preliminary stages. Focusing on the supply-side (intelligent server providers) and the demand-side (adopting enterprises), this study develops a two-layer heterogeneous complex network model grounded in complex network and evolutionary game theories. We analyze the dynamic evolutionary mechanisms and key influencing factors of strategic choices for both types of firms under different scenarios. Python-based simulations reveal that increased government subsidies, reduced intelligent server costs, higher additional benefits from transformation, and appropriate pricing strategies all promote evolutionary cooperation between the two sides. Furthermore, the network structure significantly impacts strategic selection. The model's parameters are calibrated using 2023 financial data from Foxconn Industrial Internet Co., Ltd. to anchor the simulation in a representative large-enterprise scenario. This research extends the study of intelligent transformation from a static perspective to a dynamic, spatial-relationship-aware view, and addresses the limitation of participant homogeneity by employing a two-layer heterogeneous network model, thereby providing theoretical support and context-specific insights for enterprise intelligent transformation.