The debt default risk of local government financing vehicles (LGFVs) has become a potential trigger for systemic financial risks. How to effectively prevent hidden debt risk has always been a hot issue in public-private partnership (PPP) financing management research. In recent years, machine learning has become more and more popular in the study of enterprise credit evaluation. However, most scholars only focus on the output of the model, and do not explain in detail the extent to which variables affect the model and the decision-making process of the model. In this paper, we aim to apply a better credit rating method to the key factors and analysis of LGFV’s default risk, and analyze the decision-making process of the model in a visual form. Firstly, this paper analyzes the financial data of LGFVs. Secondly, the XGBoost-logistic combination algorithm is introduced to integrate the typical characteristics of PPP projects and construct the credit evaluation model of LGFVs. Finally, we verify the feasibility of the model by K-fold cross validation and performance evaluation. The results show that: (1) net worth, total assets, operating income, and return on equity are the most critical factors affecting the credit risk of LGFVs, asset-liability ratio and tax revenue are also potentially important factors; (2) the XGBoost-logistic model can identify the key factors affecting the credit risk of LGFVs, and has better classification performance and predictive ability. (3) The influence of each characteristic variable on model decision can be quantified by the SHAP value, and the classification decision visualization of the model improves the interpretability of the model.

Over the past three decades, there have been many comprehensive studies related to public–private partnerships (PPP), but the analysis at the macro level still lacks comprehensiveness and interpretability. Through the application of bibliometric analysis, 2-mode network, and strategic coordinate analysis, we systematically analyzed the derivative characteristics of the literature data and the coupling characteristics of countries and keywords. Moreover, through the frequency and betweenness centrality, etc., this paper determines the evolution path of keywords and the evolution direction of theme words and realizes the prediction of theme words and keywords in the future. The results show that: (1) Through the three-stage biclustering analysis, we determined the hot theme words and hot keywords for each stage and focused the theme direction and main research content of the evolution, which led to great interpretability of the data analysis in the literature characteristics; (2) Through the distribution characteristics of time and space, the USA, China, the UK and other mainstream publishing countries and their main research hotspots were determined. Among them, developing countries have strong willingness in academic cooperation and great potential for academic development; (3) According to the derivative characteristics of the literature data, it is predicted that the future research hotspots are: the integration of business economy and sustainability, the integration of policy support and innovative technology application, and the urbanization promotion of developing countries. Based on the findings, this study makes concrete and targeted research methods and provides reference value and application value for the future research and analysis of PPP.