This paper proposes an order dispatch algorithm of online ride-hailing platform based on mean-field multi-agent reinforcement learning with the ability to globally perceive supply-demand dynamics. Our algorithm improves the collaboration between agents in the local area by integrating multi-agent reinforcement learning with mean-field theory， and enhances the ability of agents on perceiving and optimizing the global supply-demand gap across the global area by injecting the context about global supply-demand dynamics. Besides， we built a data-driven simulator for the training and evaluation of algorithms. Extensive experiments show that in two different scenarios of a whole day and rush hour， our algorithm significantly outperforms the existing order dispatch algorithms in terms of order response rate and accumulated drivers’ income. The experimental results convincingly validate the effectiveness of our algorithm.