Flatness is a vital quality index that determines the dimensional accuracy of the cold-rolled strip. This paper designs a local shape wave extraction algorithm and a fuzzy classification algorithm for overall flatness defect classification based on cosine distance. By introducing the small displacement buckling theory of thin plates, the plate stress buckling model of overall and local shape waves is studied, and the critical buckling elongation difference of the overall shape and the local shape under the given conditions are obtained. Finally, using the multi-objective decision-making evaluation method, a comprehensive evaluation model of the flatness quality is established. The model is applied to the actual cold rolling production. The on-site flatness data are used to verify the flatness quality determination model both locally and overall. The results show that the model can accurately identify the local and overall flatness defects of cold-rolled strips, realizes the accurate identification and evaluation of the cold-rolled flatness quality, and provides strong support for the optimization of rolling process parameters and the improvement of the quality of thin strip products.