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The rapid development of big data and cloud computing technology provides technical support for mining the rich scientific research and economic value of meteorological data. It promotes the wide application of Hadoop and Hadoop Distributed File System (HDFS) and distributed computing model in the field of meteorological data processing. Due to the 4V characteristics of big data, new data processing algorithms need to be introduced to improve the efficiency of meteorological data processing. Through the research on the principle of Classification And Regression Trees (CART) algorithm, based on Hadoop cloud platform, a random forest model is created, which provides a new possibility for the application of data mining algorithm on cloud platform. The design of meteorological big data cloud platform based on CART mining algorithm adopts Hadoop system architecture and MapReduce workflow to deploy the meteorological big data cloud platform in clusters. The overall architecture of the platform is divided into infrastructure layer, data management and processing layer, application layer, which reduces the time to establish the decision tree and realizes the big data cloud platform functions such as efficient processing and mining analysis of meteorological data.