Entity resolution is a key aspect of data integration, and also is a necessary preprocessing step of big data analytics and mining. In big data era, more and more query-driven data analytics applications come out, and query-based entity resolution becomes a hot topic. This work studies multi-attribute data indexing technology for entity cache in order to promote query-resolution efficiency. There are two core problems. One is how to design the multi-attributeindex. An R-tree based multi-attributeindex is designed. Entity cache is produced online, so an online index construction method is proposed based on spatial clustering. A filter-verify based multi-dimensional query method is proposed. It filters impossible records by the multi-attributeindex, and then verifies each candidate record with similarity functions or distance functions. The other ishow to insert different string attributes into the tree index. The basic solution is mapping strings into integer spaces. For Jaccard similarity and edit similarity, a q-gram based mapping method is proposed, and is improved by vector dimension reduction and z-order, which achieves high mapping qualities. Finally, the proposed hybrid index is experimentally evaluated on two datasets. Its effectiveness is validated, and moreover, different aspects of the multi-attribute index are also tested.