Access control technology is a security mechanism for managing users’ access to resources, which can effectively prevent unauthorized access and resource leakage. In the digital age, how to balance the relationship between information circulation and privacy protection via effective access control technology and ensure the safe and orderly flow of data elements has become a problem to be urgently addressed at present. However, the existing access control technology still has problems such as insufficient integration with trust evaluation, lack of dynamic adjustment capabilities, and difficulty in precise authorization in the research on domain data sharing scenarios. To this end, a trustworthy dynamic access control model scheme based on game theory is proposed, which integrates a three-layer collaborative mechanism of “trustworthy evaluation, dynamic adjustment, and access decision”. Firstly, based on the attribute weight algorithm, a multi-factor trustworthy prediction model is designed to calculate the trust probability of the access subject. Secondly, from the perspective of long-term stability, an evolutionary game dynamic adjustment model between the access subject and the object is built to periodically and dynamically adjust the reward and punishment incentive mechanism and access authorization threshold, thereby achieving the adaptive optimization of access control. Finally, based on Bayesian game theory, an incomplete information real-time decision-making model is built, and access control decisions are made based on the mixed strategy Nash equilibrium, with the trust degree updated by the equilibrium state feedback. The results of simulation experiments and sensitivity analysis verify that the proposed scheme can effectively improve the access control accuracy and achieve dynamic adjustment of access control strategies and accurate authorization.