Research on the Model of Deep Learning Technology in Credit Evaluation of Digital Economy

In the swiftly advancing digital economy, assessing creditworthiness has emerged as a pivotal component in gauging the financial reliability of individuals and corporations. Conventional methods for credit evaluation, relying heavily on statistical analysis and rule-based systems, struggle to address the complexities inherent in voluminous and unstructured data. This research introduces a novel creditworthiness assessment model grounded in deep learning techniques. The proposed model employs deep neural networks to analyze vast datasets pertaining to personal credit, with the primary objective of enhancing the precision and reliability of risk evaluations. Initially, the foundational concepts of deep learning are elucidated, followed by the design of a deep learning framework specifically tailored for personal credit risk analysis. Systematic training of the model is then undertaken within the context of the digital economy. Empirical evidence demonstrates that the deep learning model outperforms traditional logistic regression and decision tree models, achieving an evaluation accuracy exceeding 90% across various test datasets. Moreover, the model exhibits remarkable resilience, with an average error margin of under 0.05 across differing credit ratings. To further scrutinize model performance, its efficacy under varying data scales and noise levels is evaluated. Results affirm that the model's accuracy and stability are bolstered with increased data volume, and it maintains a low error rate even when confronted with highnoise data conditions.