Focused on the current lack of fair and reasonable incentive mechanism for federated learning， and the difficulty in measuring the contribution to federated learning by participant nodes with different data volumes， different data qualities， and different data distributions， a new incentive mechanism for federated learning based on Generative Adversarial Network （GAN） was proposed. Firstly， a GAN with Trained model （GANT） was proposed to achieve high-precision sample generation. Then， the contribution evaluation algorithm of the incentive mechanism was implemented based on GANT. The algorithm filtered samples and generated data labels through the joint model， and introduced the local data labels of the participant nodes to balance the impact of non-independent identically distributed data labels on the contribution evaluation. Finally， a two-stage Stackelberg game was used to realize the federated learning incentive process. The security analysis results show that the proposed incentive mechanism ensures data security and system stability in the process of federated learning. The experimental results show that the proposed incentive mechanism is correct， and the contribution evaluation algorithm has good performance under different data volumes， different data qualities and different data distributions.