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This paper selects the open dataset of the auction, combines the factor analysis with XGBoost, analyzes the dimension of the pre-processed high-dimensional data with factor analysis, and then uses XGBoost to predict the investment level of P2P online loan investors, and analyzes and quantifies the willingness to invest. The relationship with specific influencing factors. The main factors affecting the willingness to contribute through the ranking of the characteristic factors of the factor analysis and the reduction of the common factors include: the borrowing rate and the initial rating, the real name certification on the borrower’s mobile phone, and the repaid principal and interest paid. The model prediction results show that the XGBoost method combined with factor analysis predicts that the error rate of the investment level on the training set is train-merror: 0.03829, and the error rate on the test set is test-merror: 0.050386. Compared with the method using XGBoost alone, the prediction error rate based on factor analysis and XGBoost on the training set and the test set is reduced by 0.020706 and 0.0152, respectively, which significantly reduces the error rate; and shortens the operation time by 18 seconds. The conclusions obtained in this paper provide reference for the stable development of the P2P industry and the decision-making of investors.

P2P Online Loan Willingness Prediction and Influencing Factors Analysis Based on Factor Analysis and XGBoost