In the era when distributed computing and memory highly count, the technology of memorybased distributed computing framework, such as Spark, has gained unprecedented attention and is  widely applied. We design and implement the BIRCH algorithm parallelization based on Spark, which can maximize performance optimization and reduce the frequency of shuffling and disk accessing. We do some theory analysis and describe the DAG of the BIRCH based on Spark. Finally, we compare the performance of the parallelized BIRCH algorithm with the BIRCH algorithm of a single machine and the MLlib KMeans clustering algorithm. Experimental results show that the parallel BIRCH algorithm based on Spark obtains ideal running time and speedup without obvious clustering quality loss.