Agricultural Financing Risk Data under Smart Contracts Based on LSTM-GRU Model

Abstract

In response to the problems of low accuracy and slow decision-making efficiency in predicting agricultural financing risks, this paper combined smart contract technology and used the LSTM-GRU (Long Short Term Memory-Gated Recurrent Unit) model to analyze agricultural financing risk data. Firstly, data related to agricultural financing risks in 2022 and 2023 were collected on site, and principal component analysis was adopted for dimensionality reduction to accelerate decision-making efficiency. Then, the LSTM (Long Short-Term Memory) model and GRU (Gated Recurrent Unit) model were fused, and agricultural financing risks were predicted. Finally, smart contracts were designed to apply the predicted results of the model to actual financing decisions. By monitoring the execution process of the contract, corresponding operations were executed based on the predicted results of the model. The experimental results showed that the average accuracy of the LSTM-GRU model in predicting agricultural financing risks reached 98.64%, which was 4.29% higher than the GRU model. The decision-making speed was only 0.52 seconds, which improved the prediction accuracy and decision-making efficiency of the model.