Impact of Big Data Analysis Capabilities Powered by Decision Tree Model on Green Innovation and Corporate Performance

In the data - driven decision - making era, the impact of Big Data analytics capabilities (BDAC) on green innovation and business performance has attracted much attention. Based on the resource-based view (RBV) theory, this study explores the relationship between firm performance and green innovation. Partial least squares structural equation model (PLS-SEM) was used to analyze 413 valid questionnaires of middle and senior managers. After pre-processing, several classical machine learning models were used for analysis. The decision tree model is superior to other models in accuracy, precision, recall rate and F1 score, showing high prediction accuracy. The visualized training decision tree model clearly reveals the complex interactions between the three variables. The results show that BDAC indirectly affects firm performance by promoting green innovation, and green innovation significantly promotes firm performance. This study provides valuable theoretical insights and practical guidance for corporate decision-making and strategy development.