Abstract: Based on the big data analysis for high-speed railway disaster monitoring system, by means of distributed file system storage, MapReduce/Spark computing framework and data mining technology, this paper studied on the disaster law analysis, disaster prediction, application rule optimization, monitoring point layout optimization,equipment selection and equipment state analysis of high-speed railway disaster monitoring system. Taking the rule of strong wind and equipment running as an example to analyze, the analysis results show that about 50% of the gale is concentrated between 15 m/s and 16 m/s. By optimizing the warning threshold of strong wind, the number of gale warning can be reduced effectively. Power failure is the main failure of disaster system equipment, so it needs to be monitored and maintained. This study can solve the problems encountered in the application and maintenance of disaster monitoring system, and provide technical support for comprehensive analysis and application research of disaster monitoring data.