The construction of government-sponsored affordable housing projects requires a significant amount of capital, poses challenges in accounting and financial management, and entails high financial risks. This paper establishes a financial risk warning indicator system for affordable housing construction funding. Through the KW test, indicators that fail to pass the significance test are excluded. Factor analysis is then employed to streamline the indicators, identifying seven common factors: funding security, cost control, project management, operational efficiency, government compliance, market and external environment, and social impact. The extracted public factors are used as input variables for the BP neural network, forming a financial risk warning model for government-sponsored affordable housing construction based on the SSA-BP neural network. The financial risk prediction performance of the model was evaluated. The accuracy rate, recall rate, F1 score, and precision rate of the SSA-BP neural network model in this study were 93.2%, 96.1%, 92.8%, and 93.4%, respectively, all higher than those of the compared BP neural network model, logistic regression model, and KNN model, demonstrating excellent predictive performance.