<p>This article investigates the event-triggered model-free adaptive iterative learning cluster consensus control problem for a class of unknown nonlinear multi-agent systems. Firstly, the nonlinear system under consideration is transformed into an equivalent linearized model, and the cluster consensus error is characterized based on the communication topology. Secondly, an event-triggered condition is developed by designing an energy function along the iteration axis to conserve communication resources. Then, the boundedness of the tracking error is strictly analyzed and proved by using the contraction mapping method and norm theory. Finally, the effectiveness of the proposed algorithm is demonstrated through a numerical simulation example.</p>