Recently, a lot of works have been devoted to re- searching how agents can learn efficient cooperation in multiagent systems. However, it still remains challenging in large-scale multi- agent systems (MASs) due to the complex dynamics between the agents and environment and the dimension explosion of state-action space. In this paper, we propose a novel MultiAgent Automatic Curriculum Learning method (MA-ACL) to solve learning prob- lems of large-scale cooperative MASs by beginning from learning on a multiagent scenario with a few agents and automatically progressively increasing the number of agents. An evaluation mech- anism based on self-supervised learning is innovatively designed to automatically generate appropriate curricula with a progressively increasing number of agents. Moreover, since the observation state dimension of agents varies across curricula and the learned policy knowledge needs to be effectively encoded, we design a new Dis- tributed Transferable Relation-modeling Policy network structure (DTRP) to handle the dynamic size of the network input and model relational knowledge between agents and their surrounding envi- ronment. Simulation results show that the proposed MA-ACL using DTRP can significantly improve the performance of large-scale multiagent learning compared with manual or non curriculum learning methods, and DTRP greatly boosts the performance of MA-ACL.