The rapid development of network freight platforms has directly increased the level of social logistics resource collaboration and improved both the efficiency and quality of logistics industry services. As a bilateral platform that connects freight shippers and freight carriers, the organizational structure and operational mode of network freight platforms differ substantially from those of traditional logistics service providers. In this paper, a tripartite evolutionary game model is constructed in which a network freight platform, freight shipper, and freight carrier are considered, and the evolutionary stability strategies of the parties and the tripartite system are dynamically analyzed. The reliability of the model is verified through a numerical case, and several countermeasures have been proposed to improve the stability of the system based on the sensitivity analysis of important parameters. This paper helps standardize the principal behavior of all parties under the network freight mode, reduce the default risk of all parties, and improve the overall cooperation stability of the logistics service supply chain.

Early warnings of enterprise credit risk based on supply chain scenarios are helpful for preventing enterprise credit deterioration and resolving systemic risk. Enterprise credit risk data in the supply chain are characterized by higher‐dimension information and class imbalance. The class imbalance influences the feature selection effect, and the feature subset is closely related to the predictive performance of subsequent learning algorithms. Therefore, ensuring the adaptivity of feature selection and the subsequent class imbalance–oriented classification model is a key issue. We propose an AdaBoost ensemble model with fast nondominated feature selection (AdaFNDFS). AdaFNDFS uses the FNDFS method in the AdaBoost algorithm to iteratively select features and uses the classifier to evaluate the performance of feature subsets to train the class imbalance–oriented classifier and the best‐matched feature subset, ensuring the adaptivity of feature selection and subsequent classifiers. The further use of the differential sampling rate (DSR) method enables AdaFNDFS to integrate more training models with different knowledge and to obtain higher accuracy and better generalization ability for prediction tasks facing high‐dimensional information and class imbalance. A test using credit risk data from Chinese listed enterprises containing supply chain information demonstrates that the prediction scoring indicators, such as AUC, KS, AP, and accuracy, of the AdaFNDFS are better than those of basic models such as LR, LDA, DT, and SVM and multiple hybrid models that use SMOTE, feature selection, and ensemble methods. AdaFNDFS outperforms the basic models by at least 0.0073 (0.0344, 0.0349, and 0.0071) in terms of the AUC (KS, AP, and accuracy). AdaFNDFS has outstanding advantages in predicting enterprise credit risk in the supply chain and can support interested decision‐makers.