Loan risk analysis is a common test faced by global financial institutions. In the context of big data, it is of practical significance to prevent loan risks through machine learning algorithms. Aiming at the imbalance in loan data and high noise, this study uses the Boruta feature selection algorithm to sort the importance of loan data. In addition, it proposes the CatBoost integrated learning algorithm based on Comprehensive Learning Particle Swarm Optimization (CLPSO-CatBoost) for loan risk prediction. This algorithm improves the global search and avoids the local optimum. Compared with the traditional credit evaluation models, CLPSO-CatBoost has high accuracy.