Black-box AI systems lack transparency, making it difficult for users to verify the true operation process of the model. The introduction of zero-knowledge proof technology changes this situation. Through the ZK proof mechanism, four layers of assurance can be achieved simultaneously: verifying that the model is indeed correctly executed, ensuring the model weights remain private, proving that the output is mathematically valid, and preventing any links from being tampered with. This solution transforms untrusted AI reasoning processes into verifiable cryptographic systems, allowing users to trust the computation results without exposing underlying data or model details. This is of great significance for application scenarios with high requirements for trust and security, such as finance and privacy computing.