Abstract: When dealing with high-dimensional problems in traditional representation model technology, the number of sample points required for modeling increases exponentially due to the increase in variable dimension, which will lead to a significant increase in computational cost. In order to build a representation model suitable for high-dimensional problems, based on the DIRECT optimization algorithm, the initial sample point position is improved, the initial sample set is expanded, and the Kriging modeling method and high-dimensional model representation (HDMR), which can avoid the established model from falling into local optimality, and proposes an improved Kriging-HDMR (iKriging-HDMR) modeling method. The iKriging-HDMR modeling method uses the advantages of HDMR to equate the response function of the high-dimensional problem to a series of low-dimensional function superpositions, taking advantage of the iKriging-HDMR modeling method to reduce the number of sample points required in the modeling process number. A new convergence condition is proposed to reduce the local error of the agent model to ensure that the established agent model has high accuracy. The effectiveness of the proposed method is verified by numerical examples and robot engineering applications. The results show that the proposed iKriging-HDMR modeling method can significantly reduce the number of sample points required for modeling, and has good calculation accuracy and efficiency.