The analysis of credit governance in the digital economy development under artificial neural networks.

This paper aims to provide new avenues for innovation in credit governance in the digital economy to provide more reliable credit evaluation solutions for financial, commercial, and social interactions. This paper integrates the potential value of Internet of Things (IoT) technology in credit governance and proposes a credit governance method that utilizes IoT data and an improved Long Short-Term Memory model. The proposed model introduces an adaptive mechanism to monitor changes in data in real-time and automatically adjust network parameters to improve the model performance. Experimental validation is conducted on the University of California Irvine dataset. It is found that the proposed model performs well in terms of accuracy, F1 value, and Area Under the Curve value, with values of 0.9, 0.91, and 0.94, respectively. This indicates that the proposed model has higher classification accuracy and performance in classification tasks while maintaining efficient training time. This indicates that the presented method has potential application prospects in credit governance research.