Development of Xi'an Tourism Data Prediction Model Based on Machine Learning

This study takes Xi'an tourism as the research object, constructs a tourism data prediction model based on machine learning technology, and proposes a complete prediction framework by analyzing the core factors affecting the development of tourism, including the number of tourists, hotel occupancy rate, traffic flow, weather conditions and promotional activities. The study uses three models of support vector regression, random forest and deep neural network for comparative analysis. The results show that deep neural network has significant advantages in prediction accuracy and stability. In the process of model optimization, feature engineering, hyperparameter tuning and distributed computing technology are introduced to significantly improve the operation efficiency and prediction performance of the model. This study provides an effective tool for the scientific planning and refined management of the tourism industry, and also demonstrates the application potential of machine learning in complex nonlinear problems.