With the development of technology, hybrid processing robots have gradually become prevalent for processing large structural components. However, due to various error sources, the accuracy of uncalibrated robots may fail to satisfy actual machining requirements. In this paper, the TriMule-600 hybrid robot independently developed by Tianjin University is taken as the research object, and a data-driven error compensation method for hybrid processing robots is proposed. This method equivalently treats errors caused by the robot’s non-time-varying error sources as joint motion errors and completes error modeling for these errors. In addition, based on the implementation of joint motion error modeling, an error compensation function is constructed using polynomial functions mapping joint nominal values to joint compensation amounts. To explore the impact of polynomial function order and type on the accuracy of the compensation function, this paper not only provides a general method for constructing an nth-order polynomial but also conducts performance comparisons and selections for three different types of polynomial kernel functions, providing reference for future research.