Firstly, regional economic classification was made by clustering algorithms, which could be referenced by
Support Vector Machine classification. Then Support Vector Machine classification was made to classificatory regional
economic resources niche. Furthermore, more detailed classification was made under different economic resources niche
indicators, which solve the problem of inadequate samples, making Categories strong operational and practical
significance. Thus, more reasonable classification could be made by support vector machine model through the
establishment of relatively small samples.