Forecasting the Competitiveness of Technological Innovation Talents Using Least Squares Support Vector Machine

In this paper, we focus on the problem of forecasting the competitiveness of technological innovation talents, which is very important for modern enterprise management. To forecast the competitiveness of technological innovation talents, an index system with hierarchical structure is described, and four main categories are included, that is, 1) Technological innovation talent stock competitiveness, 2) Technological innovation talents development environment competitiveness, 3) Technology innovation talents utilization efficiency competitiveness, and 4) Technology innovation talents sustainable development competitiveness. To solve the disadvantages of SVM, LSSVM is used to map the nonlinear input data to a high dimensional feature space. Next, the LSSVM classifier is used to forecast the competitiveness of technological innovation talents. Finally, experimental results demonstrate the effectiveness of the proposed algorithm.