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For prefabricated components bolt hole nodes positioning, due to uneven lighting in the industrial environment and bolt holes are not regular smooth round, resulting in poor positioning accuracy.The paper proposed a random Hough transform circular localization algorithm introducing an improved genetic algorithm to optimize support vector regression analysis (IGA-SVR).The genetic algorithm is improved by introducing a shrinkage bracket and spiral update mechanism to enhance the local search ability of the algorithm; at the same time, the cross-variance operator is improved and a convergence factor is introduced to overcome the problem of slow convergence of the genetic algorithm at the later stage. The improved genetic algorithm is used to optimize the parameters of the support vector regression model, and the hyperplane equation approximating the bolt hole is trained by circular training samples.The three points on this model are used to locate the circle by random Hough transform, and the three-dimensional coordinates of the circle are obtained by binocular vision algorithm.The effectiveness of the proposed algorithm is verified by four standard test functions and bolt hole node positioning experiments of concrete precast component model. The results show that the improved algorithm has better optimization performance. When combined with random Hough transform positioning circle, the positioning accuracy is significantly improved and meets the requirements of engineering measurement.