Artworks face copyright confirmation and protection challenges for their easy reproduction and dissemination, and traditional similarity detection methods are not robust enough under multiple image transformations, leading to difficulties in copyright confirmation and protection. In this paper, we propose an artwork copyright confirmation and protection method, DNACP, based on the DINOv2 model and NFT technology, which aims to solve the problems of attribution confirmation and piracy identification in traditional copyright and effectively avoid redundant NFTs. To ensure the originality of artworks, the DINOv2 self-supervised learning model is used to extract features from artwork images accurately, calculate the similarity between feature vectors, and then compare with a set threshold. A digital signature will be generated when the similarity does not exceed the threshold. At the same time, using NFT technology, the digital signature and copyright information of artworks are bound to the NFT, giving the work a unique digital identity and storing it on the blockchain, thus ensuring the non-tamperability and traceability of the copyright registration. The copyright confirmation experiment results prove the effectiveness of the DNACP method.