Tourism Prediction based on Multi-source Big Data Fusion Technology

Abstract

In the practical application of existing big data tourism prediction, there are some practical problems, such as complicated data sources and difficult fusion, low prediction accuracy and poor guiding practice effect. In view of this situation, this paper intends to build a tourism big data index prediction model suitable for the characteristics of tourism development through core data extraction, multi-source data fusion, complex data modeling and other key technologies. With the help of the improved tourism prediction model based on multi-source big data fusion technology, the tourist flow and consumption characteristics of Shandong province are more accurately identified and predicted. It can provide help for optimizing public service of tourism, strengthening early warning of tourist flow and improving marketing strategy of tourist destination. This study innovatively supplements the effective integration theory of multi-source tourism big data and the organic integration theory of big data and traditional sampling survey data. At the same time, the relevant methods of tourism big data forecasting model are extended.