The generation of content across multiple fields, including text, video, code, image, and audio, is rapidly increasing [1]. For example, the DALLE-2 model converts text into images [2], and AudioLM converts text into audio [3]. Nevertheless, the review of AI-generated content (AIGC) remains at a low level, currently relying on manual audits and user awareness, which are fragmented, localized, and inefficient. Related work includes the representative benchmarks proposed by Tao Wang et al. in terms of privacy, controllability, and authenticity [4], but there is a lack of implementation platforms. Although ethical issues surrounding artificial intelligence have attracted significant attention, the review of generative AI content has received little focus due to challenges such as large data scale, data dispersion, and the absence of effective review methods. Blockchain is a decentralized accounting technology characterized by transparency, traceability, and tamper-resistance [5]. In this paper, we propose a novel model for integrating blockchain technology on a large scale with AI content review for the first time. This includes how to achieve load balancing between AI-generated content and reviewed content, how to establish copyright, track sources, and conduct privacy content review without leaking information to third parties. Additionally, we analyze the feasibility and security attributes of the aforementioned methods and propose a security assessment approach.