In the Spark computing platform, data skew often causes some nodes to withstand greater network traffic and computing pressure, which imposes a huge burden on the cluster's CPU, memory, disk, and traffic, affecting the computing performance of the entire cluster. Through the research on Spark Shuffle design and algorithm implementation, and deep analyses on the essential reasons of data skew in large-scale distributed environment, this study proposes a method to avoid data skew in shuffle process through the broadcast mechanism, analyzes the process of broadcast variable distribution logic, and gives the algorithm implementation and performance advantage analysis of the method. The performance of the method is improved by the Broadcast Join experiment.