Federated learning is a novel machine learning paradigm in which the model is trained on local data by distributed clients. Most of the current research on federated learning assumes that clients are unconditionally providing data and training models, and little consideration has been given to how to incentivize clients with high-quality data to participate in the model training task. Therefore, this paper proposes a blockchain-based federated learning incentive mechanism combining data quality verification and reverse auction. Firstly, by verifying the quality of client data, the client with high-quality data that meets the task requirements is selected, and then the client sends its bid for the task to the reverse auction smart contract. Secondly, some clients with the best performance in different training phases are selected to participate in the task training using smart contracts, and a certain number of clients are selected to form a committee among the unselected participants. The committee members are responsible for validating the local model parameters of the clients while receiving validation rewards. Finally, we conduct simulation experiments on two datasets separately, and the experimental results demonstrate the effectiveness of our proposal.