Construction of Tourism Market Trend Forecasting Model Based on Big Data Analysis

This article aims to build a tourism market trend prediction model based on big data (BD) analysis, so as to realize accurate prediction and scientific decision-making of the tourism market. In view of the huge, complex and changeable data of the current tourism market, a comprehensive BD analysis framework is proposed, which provides rich feature input for the prediction model by integrating multi-source tourism data. In the method, the random forest (RF) algorithm is used to build the prediction model, and the parallel processing ability of BD technology is used to improve the prediction performance of the model. Through experimental comparison, it is verified that the proposed model is superior to the traditional decision tree algorithm in response speed and accuracy, especially in the environment of large data volume. The experimental results show that the tourism market trend prediction model based on BD analysis proposed in this article has high feasibility and can provide scientific market trend prediction and decision support for tourism enterprises. Therefore, it is considered that the model has a broad application prospect in the field of tourism market prediction, and it is expected that the prediction performance and generalization ability of the model will be further improved by continuously optimizing the model parameters and algorithm design.