A computer-implemented action recognition method, comprising: obtaining motion data, wherein the motion data comprises first motion data, second motion data, and third motion data; the first motion data is collected by a first data collection apparatus located at a first preset part of a player; the second motion data is collected by a second data collection apparatus disposed at a preset location of a racket; and the third motion data is collected by a third data collection apparatus disposed at a preset shooting location; obtaining a gait characteristic of the player in a motion process based on the first motion data; obtaining a swing gesture characteristic of the player in the motion process based on the second motion data; obtaining an image action characteristic of the player in the motion process based on the third motion data; and recognizing a hitting action of the player in the motion process based on the gait characteristic, the swing gesture characteristic, and the image action characteristic; the method characterised in that the first motion data comprises foot acceleration data and foot angular velocity data, and the obtaining a gait characteristic of the player in a motion process based on the first motion data comprises: drawing a foot acceleration waveform and a foot angular velocity waveform based on the foot acceleration data and the foot angular velocity data, and extracting a foot acceleration waveform characteristic; determining a departure location and a touchdown location of each step based on the foot acceleration waveform characteristic; performing single-step segmentation on the foot acceleration waveform and the foot angular velocity waveform based on the departure location and the touchdown location of each step; and determining, based on a segmented foot acceleration waveform and a segmented foot angular velocity waveform, a classification of a lower limb action corresponding to each step of the player and action parameters of the lower limb action. obtaining motion data, wherein the motion data comprises first motion data, second motion data, and third motion data; the first motion data is collected by a first data collection apparatus located at a first preset part of a player; the second motion data is collected by a second data collection apparatus disposed at a preset location of a racket; and the third motion data is collected by a third data collection apparatus disposed at a preset shooting location; obtaining a gait characteristic of the player in a motion process based on the first motion data; obtaining a swing gesture characteristic of the player in the motion process based on the second motion data; obtaining an image action characteristic of the player in the motion process based on the third motion data; and recognizing a hitting action of the player in the motion process based on the gait characteristic, the swing gesture characteristic, and the image action characteristic; the method characterised in that the first motion data comprises foot acceleration data and foot angular velocity data, and the obtaining a gait characteristic of the player in a motion process based on the first motion data comprises: drawing a foot acceleration waveform and a foot angular velocity waveform based on the foot acceleration data and the foot angular velocity data, and extracting a foot acceleration waveform characteristic; determining a departure location and a touchdown location of each step based on the foot acceleration waveform characteristic; performing single-step segmentation on the foot acceleration waveform and the foot angular velocity waveform based on the departure location and the touchdown location of each step; and determining, based on a segmented foot acceleration waveform and a segmented foot angular velocity waveform, a classification of a lower limb action corresponding to each step of the player and action parameters of the lower limb action. drawing a foot acceleration waveform and a foot angular velocity waveform based on the foot acceleration data and the foot angular velocity data, and extracting a foot acceleration waveform characteristic; determining a departure location and a touchdown location of each step based on the foot acceleration waveform characteristic; performing single-step segmentation on the foot acceleration waveform and the foot angular velocity waveform based on the departure location and the touchdown location of each step; and determining, based on a segmented foot acceleration waveform and a segmented foot angular velocity waveform, a classification of a lower limb action corresponding to each step of the player and action parameters of the lower limb action.