A computer implemented method for forecasting a data capacity based on a Kalman filter, comprising: acquiring a capacity time sequence of information data; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model, comprising: constructing a system state equation under a Jerk model for the capacity time sequence, and extracting a state transition matrix A and a process noise variance matrix Q corresponding to the system state equation; performing a Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal, comprising: performing the Kalman filter estimation on the capacity time sequence by using the state transition matrix A and the process noise variance matrix Q, and generating at least one state characteristic signal of a velocity, an acceleration and an acceleration derivative; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining corresponding at least one segmentation point, comprising: calculating, for at least one of the signals of the velocity, the acceleration and the acceleration derivative obtained after the Kalman filter estimation on the capacity time sequence, variances or means of the signals within every two sliding windows according to time sequence by adopting two continuous sliding windows with the same length; marking an intermediate point between the two continuous sliding windows as a suspected segmentation point, if a probability that the variances or means of the signals within the two sliding windows are not identical is greater than a preset corresponding probability threshold; determining a signal area constituted by a plurality of continuous suspected segmentation points as at least one phase change signal area of the capacity time sequence; and segmenting the capacity time sequence according to the determined at least one phase change signal area, and determining the corresponding at least one segmentation point; and forecasting the data capacity at future time for data storage capacity expansion according to the at least one segmentation point determined in the capacity time sequence. acquiring a capacity time sequence of information data; establishing a dynamical model for the capacity time sequence, and extracting a state transition parameter and a process noise parameter of the dynamical model, comprising: constructing a system state equation under a Jerk model for the capacity time sequence, and extracting a state transition matrix A and a process noise variance matrix Q corresponding to the system state equation; performing a Kalman filter estimation on the capacity time sequence by using the state transition parameter and the process noise parameter to generate at least one state characteristic signal, comprising: performing the Kalman filter estimation on the capacity time sequence by using the state transition matrix A and the process noise variance matrix Q, and generating at least one state characteristic signal of a velocity, an acceleration and an acceleration derivative; segmenting the capacity time sequence according to the at least one state characteristic signal, and determining corresponding at least one segmentation point, comprising: calculating, for at least one of the signals of the velocity, the acceleration and the acceleration derivative obtained after the Kalman filter estimation on the capacity time sequence, variances or means of the signals within every two sliding windows according to time sequence by adopting two continuous sliding windows with the same length; marking an intermediate point between the two continuous sliding windows as a suspected segmentation point, if a probability that the variances or means of the signals within the two sliding windows are not identical is greater than a preset corresponding probability threshold; determining a signal area constituted by a plurality of continuous suspected segmentation points as at least one phase change signal area of the capacity time sequence; and segmenting the capacity time sequence according to the determined at least one phase change signal area, and determining the corresponding at least one segmentation point; and forecasting the data capacity at future time for data storage capacity expansion according to the at least one segmentation point determined in the capacity time sequence.