With the widespread adoption of mechanical cotton harvesting in Xinjiang, challenges such as data silos, delayed information sharing, and inadequate whole-process monitoring have hindered precise and transparent quality traceability from seed cotton to lint cotton. To address this, a blockchain-based quality traceability model for machine-harvested cotton was proposed by leveraging the decentralized and tamper-resistant features of blockchain technology and the real-time data acquisition capabilities of IoT devices. A logistic regression-based off-chain/on-chain collaborative data query optimization was introduced to achieve intelligent pre-caching of high-frequency data. Additionally, an access control model integrating reinforcement learning and elliptic curve cryptography was designed to enhance data security and privacy protection. The quality traceability system was developed on the ChainMaker open-source blockchain platform. Performance tests demonstrated that the system reduced query latency from 72.37ms to 60.14ms in regular scenarios and further decreased it to 32.75ms in high-frequency scenarios, with optimization efficiency improving as data volume increased, meeting real-time user query demands. In addition, through plaintext sensitivity and key sensitivity tests, confirming average ciphertext change rates of 87.78% and 82.68%, respectively. These results ensured the privacy and security of data during cross-institutional collaboration. The model established a closed-loop architecture of “IoT data collection-blockchain notarization-smart contract verification-multi-level access control” fulfilling enterprises’ requirements for privacy data permission management and secure sharing while enhancing information retrieval efficiency.