▎ 摘　　要

NOVELTY - The method involves analyzing and generating data. A machine learning model is trained based on a regression or classification algorithm to build a learning model. Correlation between chemical vapor deposition (CVD) conditions and graphene specification results is modeled. A neural network model is provided with a region based convolution neural networks (R-CNN). The CVD conditions are included with temperature, annealing time, hydrogen supply and growth time. The graphene specification result data is included with size, coverage, domain density, aspect ratio and deviation of graphene. USE - Method for constructing a correlation modeling system between CVD conditions and graphene specification results based on machine learning. ADVANTAGE - The excessive time and cost for individual CVD experiments and analysis of the specifications of synthesized graphene are saved. DETAILED DESCRIPTION - An INDEPENDENT CLAIM is also included for an apparatus for constructing a correlation modeling system between CVD conditions and graphene specification results based on machine learning. DESCRIPTION OF DRAWING(S) - The drawing shows a schematic representation of the CVD process conditions and a CVD system.