Surface measurement using a Coordinate Measuring Machine(CMM) is the main method of processing large-aperture high order aspheric mirror fabrication. Because three main types of measurement error exist in original data, the achieved surface residual cannot be directly used in mirror fabrication. Therefore, a series of algorithms for cleaning the errors of CMM point clouds is proposed. Firstly, Probe Radius Compensation(PRC) based on aspherical surface reconstruction is used to compensate for probe radius error in acquired data. Then, the compensation data is processed to remove the rotation and translation errors in the coordinate system. Finally, Surface Residual Denoising(SRD) based on KNN is used to denoise the extracted surface residual data. In the PRC algorithm, a high-precision surface fitting model for probe center points is proposed, which takes rotation and translation errors into consideration. With this model, a correcting vector for each point can be calculated to compensate for the probe radius error. The Simulation experiments show that with PRC algorithm, the precision can reach RMS<4 nm. By densifying the sampling points with spatial interpolation and optimizing the noise characterization, the proposed SRD algorithm can identify the noise points with a high degree of sensitivity, making the denoising intelligent. Software was constructed according to the error cleaning algorithm and its application shows that it can effectively improve the accuracy and efficiency of CMM point cloud processing during aspheric mirror fabrication.