The invention discloses an aspheric measurement return error removal method and device based on a graph-to-graph neural network. The method comprises the steps of building a virtual interference
system, making a graph-to-graph return error removal
data set, building a graph-to-graph neural
network model, training the graph-to-graph neural
network model and solving an actual aspheric
surface shape error. According to the method, the computer is used for simulating the optical
system of the non-zero compensation method and generating the
data set, training of the graph-to-graph neural network is completed, convenience and rapidness are achieved, backhaul errors can be eliminated without any priori knowledge and preprocessing for interference
wavefront data of a group of actual optical systems, and good universality is achieved; a graph-to-graph neural network is used for eliminating return errors, high-frequency information in an interference
wavefront graph and
surface shape distribution of a detected aspheric surface is reserved, and the detection precision is higher; in the solving process, inherent
system errors do not exist, the solving precision does not depend on the modeling precision of the system, the complex adjustment and calibration process is avoided, and rapid and high-precision aspheric
surface shape error measurement is achieved.