In view of financial data obtained from Shanghai-Shenzhen A share listed companies of manufacturing industry with class imbalance, in order to predict the credit default of the listed companies of manufacturing industry, Lasso-Logistic model based on under-sampling improvement is proposed. Firstly, by calculating WOE and IV values, the variables with poor risk identification ability and poor stability are eliminated, then, from "data" level, the existing Lasso-Logistic model is processed for batch under-sampling, finally, the improved effect of the model is studied by simply mean integration of the prediction probability of Lasso-Logistic sub model based on "algorithm" level. Results show that from the perspective of the model holistic effect measurement indicator AUC value and distinguishing degree indicator KS value, Batch-US-LLR model with variables screening ability based on under-sampling improvement can effectively improve the effect of enterprise credit risk default measurement and have the feasibility and validity for perfecting early-warning mechanism for enterprise risk and promoting default risk identification ability.