Abstract:

The insufficient penetration of full chain information and the difficulty in balancing data sharing and security make it difficult to effectively promote the application of supply chain financing in the agricultural field. The combination of blockchain and federated learning can fully leverage the technological advantages of both， effectively compensating for the shortcomings of blockchain in data privacy and security， and federated learning in data storage， synchronization， and tamper prevention. However， it still faces problems such as low computational efficiency and insufficient incentive mechanisms. Receivable financing business is taken accounts as the research object and an agricultural supply chain financing system empowered by hierarchical federated learning and blockchain is constructed； On this basis， based on deep learning auction algorithms， the financial resource allocation strategy is constructed as an auction model with the goal of maximizing seller returns， in order to achieve individual rationality， incentive compatibility， and maximization of cooperative auction revenue for financial institutions. Research has shown that： （1） through auction strategy optimization， auction mechanisms based on deep learning can maximize seller returns； （2） The auction revenue of cooperatives is directly proportional to their data coverage and the data coverage requirements of financial institutions； （3） The higher the approximate optimization rate of neural networks， the greater the difficulty of system optimization. Therefore， cooperatives need to invest more computing resources and training time， which in turn affects their profits. New ideas for promoting the application of digital intelligence technology in the field of agricultural supply chain financing are provided， and certain practical significance is given in promoting data information security and sharing， fund integration and optimization， and assisting rural industrial revitalization.

Key words: accounts receivable auction financing, blockchain, federated learning, deep learning