Blockchain technology is celebrated for its transparency and immutability, revolutionizing trust models. However, its decentralized nature raises concerns about potential inclusion of malicious or illegal content. This study focuses on Ethereum’s blockchain, proposing an algorithm for data identification and restoration. We successfully recovered 175 files, 296 images, and 91,206 texts. Employing FastText for sentiment analysis, we achieved 0.9 accuracy after parameter tuning. Classification revealed 70,189 neutral, 5,208 positive, and 15,810 negative texts, aiding in identifying sensitive or illicit information. Our findings expose benign and harmful content coexisting on Ethereum, including personal data, explicit images, divisive language, and racial discrimination, notably targeting Chinese government officials. This study provides valuable insights for public understanding and regulatory guidance on blockchain technology.