Financiability is a prerequisite and foundation for the sustainable development of PPP. In order to prevent serious overfitting and errors caused by secondary processing in the evaluation process, the ensemble LightGBM-Blending algorithm is proposed to address the problems of high industry variability and too many evaluation category-type fields in the financiability of PPP projects. Based on the social capital perspective, we construct a financiability evaluation system, divide the training set by industry with the integrated idea, and build a multi-base classifier based on the LightGBM algorithm to avoid secondary processing of category features; finally, we crawl project data in the CPPPC through a web crawler for model training and testing, and design the blending algorithm for model fusion and optimization. The experimental results show that: compared with the traditional ensemble algorithms, the ensemble LightGBM-Blending algorithm has higher evaluation accuracy and can effectively solve the problems such as high industry variability; the blending fusion strategy improves the evaluation accuracy about 5.76% compared with other empowerment fusion strategies; a low sample industry and a high idiosyncratic industry sample is introduced for validation, and the test results show that the proportion of the low error interval and the medium error interval samples is about 79.5% and 78.26%. The model has good generalization ability, it can be effectively used for PPP financiability evaluation in different field.