Abstract: To solve the problem of position and posture estimation of wind turbine blade automatic laminating equipment,the position and posture sensor is used for information fusion,and the Invasive Weed Optimization-Particle Swarm Optimization Algorithm（IWOPSO）is proposed to support the algorithm. Support Vector Regression（SVR）is the basic algorithm and is used for SVR parameter optimization. Firstly,the Gaussian kernel function is used to carry out the high-dimensional mapping of the position and posture of the layering equipment. Then the IWO-PSO is used to enhance the global search ability of the algorithm,and the ability of PSO to find fitness is optimized by IWO’s intrusion features.,and finally the optimal parameters of the SVR are obtained. Compared with the convergence speed,the speed is increased by 20%. The experimental results show that the algorithm can estimate the position and posture of the layering equipment efficiently and quickly. The new algorithm can find the inverse kinematic numerical solution of the layering equipment without other requirements. The posture error is less than 0.2 rad. The position error is less than 0.03 m,which improves the accuracy of posture prediction and has high engineering application value and economic value.