Research on Supply Chain Data Security Optimization Algorithm Based on Artificial Intelligence

The paper has considered the blockchain and AI-based supply chain data security optimization algorithm. Massive data covering production, transportation, warehousing, and other links is collected from numerous participants of supply chains, and then the random forest algorithm is used to analyze and process it. The experimental results obtained show that this random forest algorithm identifies and classifies anomalies to the supply chain dataset with good performance, especially in classifying high-security data. In analysis, these data distributions and feature distributions may reflect differences in security levels between links within the supply chain. It assumes a normal distribution of anomaly scores, indicating that most data is usually of high quality, but the data anomalies are more prone to warehousing and retail links. This research enhances the degree of intelligent supply chain management by blockchain and artificial intelligence technology and significantly improves the security and credibility of data. The results of the research suggest that blockchain and AI integrated technology can effectively enhance supply chain data on transparency and efficiency, giving new solution spaces and ideas for supply chain management.