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With the rapid development of blockchain-based agricultural product traceability systems, blockchain query capabilities face great challenges. For supply chain participants, most of the data stored in the blockchain are coded or serialized data, which makes the process of multi-condition query such as audit and supervision of supply chain participants very difficult. In general, native blockchains do not provide a query method to satisfy multi-condition queries. Therefore, in order to realize multi-condition query and improve query efficiency, an optimization method for agricultural product traceability data was proposed. Firstly, the method used an optimized Merkle tree structure (n-Tree) to reconstruct the transaction information, so as to provide more efficient conditional verification ability. Secondly, the adaptive multi-condition block Bloom filter was used to judge the existence of query conditions in the transaction information, and then the blocks were quickly filtered. Finally, an index construction method using time weight and transaction number based heap structure was proposed, and the block number index list related to the main condition was constructed in the order of block weight. The process of querying product data included traversing the block index list, filtering irrelevant blocks, and validating specific query conditions to obtain conditional query results. The experimental results showed that the query method proposed can effectively solve the problem of conditional query in the supply chain of agricultural products. At the same time, the query time consumption was maintained at about 15ms, and the query efficiency was improved by 60.9% compared with Merkle semantic trie method and 87.7% compared with original traverse method.