With the emergence of multiprocessors, parallel technology has attracted widespread attention and become an important technology to speed up the processing of problems. Nevertheless, the use of parallel technology to speed up computing has also led to a sharp increase in the number of processors and a significant increase in parallel costs. To solve this problem, by studying the shortcomings of three parallel maximum search algorithms based on PRAM (Parallel Random Access Machine), a parallel maximum search algorithm based on data partitioning method is proposed, which is better than the balanced tree algorithm, fast search method and double logarithmic depth tree method. The maximum searching algorithm based on data partitioning method effectively solves the problems of uneven workload allocation, excessive demand for processors and harsh implementation conditions in existing parallel methods. This provides a direction for similar parallel algorithms to reduce parallel costs.