In the context of the integration of distributed cloud computing and SDN, artificial intelligence driven network architecture faces challenges such as inefficient dynamic resource allocation, difficulties in cross domain collaboration, and privacy utility imbalance. This study is based on game theory and generative AI to construct an analytical framework. By comparing Nash non cooperative, Stackelberg master-slave, and collaborative R&D models with the HJB equation using differential game models, it is found that R&D investment subsidies can enhance the R&D efforts and returns of enterprises and academic research institutions, and government R&D efforts are not affected by cost sharing; The collaborative cooperation model achieves Pareto optimality between the three parties' R&D efforts and total revenue. Combine Shapley value method to optimize the profit distribution of collaborative mode, and verify the theoretical consistency through Matlab simulation. Research the construction of three collaborative mechanisms: coordination of benefit distribution, sharing of resource investment, and incentive constraint guarantee, and propose multi-party countermeasures. The study found that the profit distribution ratio of the collaborative mode is about 24:24:52, and the profits of all parties are higher than those of other modes; In the future, it is necessary to expand the optimization of generative AI and distributed cloud computing SDN architecture to enhance the efficiency of technological collaborative innovation.

Zhenlin Jin. Research on Distributed Content Collaborative Caching Optimization Based on Multi-Agent Reinforcement Learning in Intelligent Identification Network. International Journal of Big Data Intelligent Technology (2025), Vol. 6, Issue 2: 156-164. https://doi.org/10.38007/IJBDIT.2025.060217.

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