A space partitioning method for a database table, comprising: acquiring a plurality of groups of data by pre-partitioning, based on a time stamp of data in a database table, the data in the database table according to a predetermined period length; determining a first data amount within a first time period and a second data amount within a second time period of the database table based on the plurality of groups of data, wherein the first time period and the second time period are prior to a target time period corresponding to data to be stored; calling a target network model, inputting the first data amount and the second data amount into the target network model, and outputting a third data amount within the target time period, wherein the target network model is configured to predict a data amount of a next time period based on data amounts of a previous time period and a current time period; determining a number of target regions based on the third data amount within the target time period, and partitioning , based on the number of target regions, a space in the database table configured to store the data to be stored; wherein determining the number of target regions based on the third data amount within the target time period comprises: determining the number of target regions by formula: k = ⌈ n m ⌉ based on the third data amount within the target time period; wherein k is the number of target regions, n is the third data amount within the target time period, m indicates a maximum storage capacity of a single area, and "┌ ┐" represents a rounding-up operation. acquiring a plurality of groups of data by pre-partitioning, based on a time stamp of data in a database table, the data in the database table according to a predetermined period length; determining a first data amount within a first time period and a second data amount within a second time period of the database table based on the plurality of groups of data, wherein the first time period and the second time period are prior to a target time period corresponding to data to be stored; calling a target network model, inputting the first data amount and the second data amount into the target network model, and outputting a third data amount within the target time period, wherein the target network model is configured to predict a data amount of a next time period based on data amounts of a previous time period and a current time period; determining a number of target regions based on the third data amount within the target time period, and partitioning , based on the number of target regions, a space in the database table configured to store the data to be stored; wherein determining the number of target regions based on the third data amount within the target time period comprises: determining the number of target regions by formula: k = ⌈ n m ⌉ based on the third data amount within the target time period; wherein k is the number of target regions, n is the third data amount within the target time period, m indicates a maximum storage capacity of a single area, and "┌ ┐" represents a rounding-up operation. determining the number of target regions by formula: k = ⌈ n m ⌉ based on the third data amount within the target time period; wherein k is the number of target regions, n is the third data amount within the target time period, m indicates a maximum storage capacity of a single area, and "┌ ┐" represents a rounding-up operation. <EOS>