The Chien search process is an important component of the BCH decoder in NAND flash memory.Parallel Chien search has lower latency and can run at high speed,but higher complexity restricts its app lication.In order to reduce the complexity of parallel Chien search,an improvement and optimization method of parallel Chien search is proposed.Firstly,the traditional parallel Chien search and related references are analyzed and studied.Then,the search range of common subexpression is extended,and the relevant calculation is merged.Finally,iterative matching al gorithm is improved,and an improved iterative matching algorithm based on two-dimensional search is proposed.The experimenta l results show that this method can achieve good optimization results,and effect ively reduce the complexity of parallel Chien search.Compared with the tradition al parallel Chien search and two kinds of the parallel Chien search in the related references,the optimization rates of this method approximate to 85.4%,38.7% and 29.2% respectively after the 32-bit pa rallel Chien search of BCH (2047,6,11) has been optimized.This method can better adapt to parallel Chien search archite ctures with various parallel factors and various error correcting capability.