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For the application of artificial intelligence and big data technology in geological hazard monitoring and forecast, a geological hazard data storage strategy is built based on distributed file system (HDFS) and column storage non-relational database (HBase). The data type, data format, data capacity, data frequency and data growth rate of geological hazard monitoring system and geological hazard prediction system are analyzed. Data is classified and organized from the perspective of data granularity, and different storage modes are designed for different granularity data to achieve efficient access efficiency. Data is classified according to the application characteristics of data, and different storage structures and access interfaces are provided for different types of data to obtain optimal data access performance.