Abstract: The constraints of data protection have restricted data within different enterprises and organizations, forming many "data islands" that make it difficult to tap into their inherent important value. The emergence of federated learning (FL）has made data sharing between organizations possible, but issues like unclear benefit distribution schemes, high communication costs, centralization, etc. make it difficult to meet the multi-faceted demands of data trading scenarios. To address these issues, a multi-technology fused data trading method (MTFDT) based on federated learning is proposed. In this method, the incentive mechanism is designed by combining trusted execution environments with Shapley Value, and the model and data synchronization mechanism during trading is optimized with a tree-based topological structure-based model synchronization scheme, reducing the synchronization time complexity from linear to logarithmic. At the same time, a blockchain-based benefit distribution data and model data storage solution is designed, making the transaction information tamper-proof and accountable through traceability. Finally, simulations and comparisons were conducted based on public datasets. The experimental results show that MTFDT can achieve precise evaluation of model training effects and improve the fairness of benefit distribution. Compared with existing solutions, the time consumption of model synchronization is reduced by up to 34%, and the bandwidth requirement is lower. This further verifies the effectiveness of the proposed scheme in data trading scenarios.