The positioning accuracy is a key index to measure the performance of the robot. This paper studies the positioning accuracy of the main pouring mechanism of the hybrid truss pouring robot and analyzes that the main error sources affecting the positioning accuracy are machining error, assembly error, and thermal deformation error. Error transfer matrix is constructed to describe the influence of machining errors and assembly errors on the position and pose of the terminal, and the error parameters have physical significance. The probability distribution of sensitive errors is discussed. A joint regression prediction model based on sensitive error sets is established to determine the thermal deformation error on the basis of fully considering the contribution rate of component error. The results show that the position error has a wide range of influences on the end pose, but the angle error is more sensitive, and the probability distribution of the sensitive error is concentrated. The reliable data can be obtained without reorganizing the measurement in the calibration process. The joint regression model considering the contribution rate of component error can effectively eliminate the collinearity problem in the prediction of thermal deformation from a single heat source. Compared with the single regression model, it has better prediction accuracy and effect.