The existing cross-level High Utility Itemsets Mining （HUIM） algorithms consume a lot of time and occupy large amounts of memory. To address these problems， a Data Index Structure Cross-level High utility itemsets mining （DISCH） algorithm was proposed. Firstly， the utility list with taxonomic information and index information was expanded into Data Index Structure （DIS） to efficiently store and quickly retrieve all itemsets in the search space. Then， in order to improve the memory utilization， the memory occupied by the utility lists that do not meet the conditions was reclaimed and reallocated. Finally， during the construction of utility list， early termination strategy was used to reduce the generation of utility list. Experimental results based on real retail datasets and synthetic datasets show that compared with the CLH-Miner （Cross-Level High utility itemsets Miner） algorithm， DISCH reduces the running time by 77.6% on average and the memory consumption by 73.3% on average. Therefore， the proposed algorithm can search the cross-level high utility itemsets efficiently and reduce the memory consumption of algorithm.