Application of big data in tourism market trend forecasting and benefit analysis

This study intends to explore the application and benefit analysis of big data technology in tourism market trend forecasting. By analyzing the tourist behavior data, booking records, user ratings and demographic characteristics data of the tourism platform, the multivariate linear regression model and time series analysis (ARIMA) are combined to accurately predict the changes in tourism market demand. The results of the study confirm that the use of big data analysis can effectively improve the accuracy of tourism market demand forecasting, thereby promoting the rational optimization of resource allocation and achieving the growth of corporate operating efficiency. Big data technology has further promoted the sustainable development of the tourism industry. Through effective tourist flow management, it prevents excessive consumption of scenic spot resources and implements a personalized recommendation system to improve tourist satisfaction and loyalty. It has created a higher customer return rate for tourism companies. Big data technology not only improves the prediction level of the tourism market, but also provides scientific guidance for corporate decision-making, and promotes the tourism industry to achieve intelligent and sustainable development.