This article explores the intricate relationship between financial literacy and household entrepreneurial decision-making, with a specific focus on the context of China. As China transitions towards a more innovation-driven economy, household entrepreneurship plays a vital role in job creation, income generation, and economic restructuring, particularly in rural areas. Drawing upon a comprehensive review of recent literature, this study synthesizes existing evidence to illuminate how varying levels of financial literacy influence an individual's or household's propensity to engage in entrepreneurial activities, their risk-taking behavior, access to financing, and ultimately, entrepreneurial performance. The discussion highlights the mediating roles of credit constraints and risk preference, and the emerging impact of digital finance. The findings offer valuable insights for policymakers aiming to foster a robust entrepreneurial ecosystem and promote inclusive economic growth in China.

This study proposes a deep learning framework for forecasting bank profitability by integrating macroeconomic indicators with firm-level financial data. Using models such as LSTM, GRU, Transformer, and TCN—compared against traditional approaches like Linear Regression, Random Forest, and XGBoost—we evaluated predictive performance across multiple metrics. Results show the Transformer model achieved the best performance, with an R² score of 0.95 and 91% directional accuracy, outperforming all other models. The inclusion of macroeconomic variables significantly enhanced prediction accuracy. These findings highlight the effectiveness of attention-based deep learning models for financial forecasting in dynamic economic environments.