Abstract: Agricultural product brands can severely dominate their market competitiveness. However, some challenges still remained in the supply chain of the agricultural products, such as the information opacity, rampant fake reviews, and less credibility of the brands. In this article, a credit management model was proposed using blockchain and virtual cooperatives. A transparent and trustworthy credit management system was established for the agricultural product brands in order to enhance the market credibility and competitiveness of the agricultural product brands. There was also a social relationship network in agricultural products. The results showed that the branded agricultural products followed a "government regulation - brand operator-led - multi-entity collaboration" model, thus involving multiple stakeholders, such as the farmers and cooperatives. A virtual cooperative trust management model was constructed under blockchain using this network. A density peak clustering algorithm was introduced to filter out the false information. A dual-layer credit quantification system was constructed using topological potential theory. A credit-weighted mixed consensus algorithm was designed using a blockchain structure with traceability information and credit management. The Density Peak Clustering (DPC) algorithm was determined to filter the false information. The cluster centers were used to calculate the local density of the data points and their distance to high-density points. The cluster centers of the agricultural products were accurately located using strategy graphs, effectively identifying malicious reviews even with a few parameters. Extreme content and repetitive expressions were characterized to aggregate and then removed by the algorithm. The true preferences of the real group were preserved for the authenticity and reliability of the review data. The dual-layer credit quantification was constructed after credit calculation using topological potential theory. The influence of the nodes was measured within the community using factors such as the interaction frequency and quality with the other nodes. Thereby, the individual credit was quantified with the higher influence nodes as the higher credit. Between communities, the radiation of group credit was evaluated using the topological potential field, visually demonstrating the degree of the mutual influence of the credit between different communities. The coordinates were measured on the micro and macro group credit. The credit-weighted mixed consensus algorithm was designed with the virtual cooperatives to consider the factors, such as the node credit levels and computing power, to allocate weights. The nodes with high credit and strong computing capabilities were defined as having a greater influence in the consensus, thereby improving the consensus efficiency and security. The credit data was stored in a blockchain credit block after consensus, indicating the immutability and traceability. According to the needs for the agricultural product quality, safety traceability, and credit management, a single-tree multi-type block structure of the transaction storage was designed to accommodate both traceability information and credit management in conjunction with the storage of the Hyperledger Fabric, fully meeting the actual business requirements. Experiment results show that this credit model was sensitive to the false information, while the credit of the malicious nodes was improved slowly. Once the proportion of the malicious nodes reached 50%, the recognition rate remained above 56%. In a gigabit local area network, the CWHC consensus algorithm maintained the stable consensus delay in a 4-consensus-node network with 1 abnormal node. In the 60 transaction records, the communication volume is 25% of the practical Byzantine fault-tolerant mechanism. This credit management with the blockchain and virtual cooperatives was performed best in the supply chain of the branded agricultural products. The finding can also provide a feasible path for the brand credit management of the agricultural products.