Change address identification is one of the difficulties in bitcoin address clustering as an emerging social computing problem. Most of the current related research only applies to certain specific types of transactions and faces the problems of low recognition rate and high false positive rate. We innovatively propose a clustering method based on multi-conditional recognition of one-time change addresses and conduct experiments with on-chain bitcoin transaction data. The results show that the proposed method identifies at least 12.3\% more one-time change addresses than other heuristics.