Aiming at the problems of low query efficiency and limited query types in on-chain data querying of blockchain systems， an inter-block index model was proposed. Firstly， for discrete attributes within blocks， Inverted Bloom Filters （IBFS） index was introduced. When querying data using IBFS， it allows for locating target block in O（1） time complexity without all block traversal. Secondly， for continuous attributes， the clustering algorithm was employed to calculate fine-grained data distribution intervals in block， and Dual-Layer Clustering Chain （DLCC） index was constructed by combining with maximum/minimum values of the data in block， thereby enabling the filtering of more non-target blocks during querying data. Finally， based on the proposed index model， various query algorithms were designed and implemented. Experimental results show that compared with tree Bloom filter index， IBFS index reduces the storage space by 51.0%， and shorten the time to locate target blocks by 75.9%； compared with start-end interval index， DLCC index reduces the number of located blocks during range query by 55.5%.