Implementation of Improved SVM Algorithm in Tourism Economic Data Analysis

As the field of spatial data mining, tourism geo-economic analysis and prediction can be summed up as a regression prediction problem of time series. According to the characteristics of tourism geographic economy and the demand of prediction management, this paper analyzes the spatial distribution of scenic spots and its impact on tourism geographic economy from the perspective of spatial data mining, and uses the improved SVM algorithm to analyze and predict tourism geographic economy. Then, the design and implementation of the data mining software of tourism geographical and economic prediction based on SVM are performed. The data layer is established based on large-scale relational database SQL Server 2016; the middle layer uses MATLAB to execute algorithm coding operation based on support vector regression machine, ADO and other data access components. The results of numerical experiments and prediction platform show that the scheme can generate all kinds of tourism geo economic data summary tables after various data collection, and generate prediction information for decision-making reference through algorithm operation.