Research and Application of Random Forest Algorithm in the Evaluation Index System of Tourism

Abstract

The evaluation of tourism performance is crucial for sustainable development and strategic management in the tourism sector. This study explores the application of the Random Forest algorithm to develop a comprehensive evaluation index system for tourism. Leveraging a diverse dataset encompassing visitor statistics, financial records, environmental impact data, satisfaction surveys, and social media feedback, the study employs rigorous methodologies including data preprocessing, model training, and validation. Results indicate that the Random Forest model achieves high precision and accuracy in predicting key tourism metrics. The mean squared error (MSE) for regression tasks is 0.015, with coefficient of determination ( ) values of 0.92 for visitor satisfaction and 0.89 for economic impact, highlighting the model's robust predictive capabilities. Feature importance analysis identifies visitor spending, environmental quality indices, and social media sentiment scores as pivotal factors influencing tourism performance. In classification tasks, the model exhibits 88% accuracy, with high precision (0.87), recall (0.85), and F1 score (0.86), indicating its effectiveness in predicting visitor recommendations. Cross-validation confirms the model's reliability across different datasets, with consistent ????² scores (average of 0.90) and MSE (average of 0.018). The integration of sentiment analysis from NLP enhances the evaluation system by capturing real-time visitor sentiments. These findings underscore the Random Forest algorithm's efficacy in transforming traditional tourism evaluation methods, offering a robust framework for data-driven decision-making and strategic planning in the tourism industry.