Capacity expansion resource prediction method and system for Hadoop data platform

The invention relates to a capacity expansion resource prediction method and system for a Hadoop data platform. The method comprises the following steps: training a prediction model based on historical sample data of a log library; inputting the data set of the current specific time period into the prediction model to obtain a prediction data set of the next specific time period; dividing the data of the data set in the current specific time period into in-range data and out-of-range data; dividing the data in the range into low-heat-intensity access data and high-heat-intensity access data; and calling out the low-thermal-strength access data from the HDD and the DDR and storing the low-thermal-strength access data in the SSD, and calling out the high-thermal-strength access data from the HDD and the SSD and storing the high-thermal-strength access data According to the method, the data in the next specific time period is predicted, so that the data storage structure is optimized, the access speed is increas