Massive meteorological data is limited in storage and analysis on the traditional WebGIS platform. A distributed computing and storage framework based on Hadoop to manage and analyze a large number of meteorological data was proposed. The HDFS distributed file storage framework was used in Hadoop ecosystem to store and manage massive meteorological data. In the aspect of parallel computing and analysis of massive data, MapReduce was used as the basis of distributed computing programming model. This model can make decision for agricultural production by analyzing massive climatic data. The application of regional large data decision analysis suitable for crop growth and the analysis of large data for meteorological disaster assessment were tried out. It had great application value for the research of climate change information extraction and analysis in agricultural production decision-making and other fields. Finally, the front-end pages displayed the analysis results in three-dimensional form by using the geographic information system spatial visualization technology, which made the analysis results more intuitive, and easier to analyze and decision-making, and then the impact of size of data and the number of nodes in the cluster on computing time-consuming was analyzed and compared, and the configuration was tuned the most efficient. Experiment results showed that using distributed multi-node cluster can effectively improve the storage and calculation efficiency of massive meteorological data, and solve the limitations of traditional WebGIS platform.