In this study, a line laser on-machine measurement experimental platform integrating aviation blade detection and processing is designed and built to solve the problems of low efficiency and easy surface damage during the contact measurement of aviation blades. In laser noncontact measurements, point cloud data are prone to errors. Herein, the main influencing factors involved in the measurement process are discussed and analyzed to improve the accuracy of laser in machine measurement. Furthermore, error prediction models based on radial basis function neural network and support vector regression are established and the performances of these two prediction models are compared. The error compensation strategy of free-form surface detection is used to complete the compensation and correction of point cloud data. Finally, taking a certain type of aviation blade as an example, the experimental results show that the proposed method can improve the accuracy of point cloud data by 39.86% and verify the feasibility of the error compensation model and compensation strategy.