An embodiment of the invention discloses a fabric flatness
objective evaluation method and a fabric flatness
objective evaluation device on unsupervised
machine learning, wherein the method comprises the steps of acquiring sample data in a standard evaluation environment; preprocessing the acquired sample data, eliminating
background information and interference information of an image; vectorizing the preprocessed data by means of
computer image processing technology; classifying the vectorized data, and generating a characteristic reference set; and performing image
class prediction on the characteristic reference set, thereby obtaining an
evaluation result. In the fabric flatness
objective evaluation method and the fabric flatness objective evaluation device, through extracting and abstracting a bottom-layer characteristic, a fabric image is vectorized; clustering is performed according to the characteristic of the fabric image, and a
label is set for a clustering result. Through unified extraction and abstraction on the bottom layer characteristic and objective reference classification, fabric grade prediction is performed, thereby obtaining an
evaluation result in a more fair and objective manner, reducing an error caused by artificial adoption of data for training, and furthermore preventing a subjective error caused by artificial evaluation.