Abstract: For the industrial complex processes featured with multi-extremums of output quality characteristics and nonlinear relationship between parameters and characteristics, how to optimize the parameters via small sample experiments and global modeling is critical to product quality improvement. This study proposes an approach for randomly-oriented sequential design and regression modeling of complex processes. Firstly, an initial sample is generated by using uniform design and a primary model is set up by using Support Vector Regression (SVR). Secondly, new points are added according to the generation population of Genetic Algorithm (GA) and the SVR model is rebuilt correspondingly. Thirdly, the second stage is iterated sequentially until the mean square error of successive SVR models is reached to a certain lower limit. Finally, the global optimum of characteristics and therefore, the parameters are optimized by applying GA on the latest SVR models. The theoretical analysis and the simulation study show that, compare with the one stage design and modeling approach, the proposed approach requires a smaller sample size and can get a smaller prediction error by adding new points sequentially and compare with the path-oriented sequential design and modeling approach, the proposed approach can effectively avoid the limitation of reaching local optimums by adding new points randomly.