Design of Credit Bond Default Risk Measurement Model Based on Spatio-Temporal Attention Network and Genetic Algorithm Driven by Digital Economy

With the rapid advancement of the digital economy, the financial market confronts unprecedented complexity and interconnectivity, rendering the precise prediction of credit bond default risk particularly crucial. This paper introduces a novel credit bond default risk measurement model (GST-GRU) predicated on a spatio-temporal attention network and genetic algorithm, designed to enhance the accuracy and robustness of risk prediction. Initially, data preprocessing is undertaken, encompassing time series data cleaning, completion of missing values for historical financial information and bond default statuses, and extraction of spatial discrete information through independent vector coding. Subsequently, a spatio-temporal attention mechanism is employed to amplify feature information in both domains, while the GRU network captures the long-term dependencies within the time series data. Thereafter, model parameters are refined using a genetic algorithm to ensure global optimality. Experimental results demonstrate that the GST–GRU model markedly improves prediction accuracy across multiple public and self-constructed datasets, surpassing traditional models. This research furnishes robust technical support for risk management in the financial market, fosters the evolution of credit bond default risk prediction technology, and lays the groundwork for the intelligence and automation of future financial systems.