A checkout method comprising: step S 1, acquiring an identity information of a purchaser; step S2, identifying a take-up action or a put-back action of the purchaser to acquire an action identification result, and items at which the take-up action or the put-back action aims to acquire an item identification result; and step S3, performing checkout based on the identity information of the purchaser, the action identification result, and the item identification result; wherein identifying the take-up action or the put-back action of the purchaser in the step S2 specifically comprises: acquiring a plurality of frames of consecutive hand images of the take-up action or the put-back action of items of the purchaser's hand of the purchaser in front of a shelf bearing the items, and establishing a motion track of a hand for the plurality of frames of consecutive hand images on a timeline; identifying the action of the purchaser as the take-up action or the put-back action according to a movement direction of the motion track of a hand relative to the shelf and characterized in that identifying the item at which the take-up action or the put-back action aims to acquire the item identification result in the step S2 specifically comprising: S21, acquiring a plurality of primary classification results, according to the acquired plurality of frames of hand images of the purchaser in front of the shelf bearing the items and a pre-trained first-level classification model, in which the pre-trained first-level classification model is a model that is constructed by an image identification technique of a convolutional neural network and trained by using data of all the items on the shelf; S22, acquiring a first-level classification result according to the plurality of primary classification results and a pre-trained first-level linear regression model; and S23, obtaining the item identification result according to the first-level classification result. step S 1, acquiring an identity information of a purchaser; step S2, identifying a take-up action or a put-back action of the purchaser to acquire an action identification result, and items at which the take-up action or the put-back action aims to acquire an item identification result; and step S3, performing checkout based on the identity information of the purchaser, the action identification result, and the item identification result; identifying the take-up action or the put-back action of the purchaser in the step S2 specifically comprises: acquiring a plurality of frames of consecutive hand images of the take-up action or the put-back action of items of the purchaser's hand of the purchaser in front of a shelf bearing the items, and establishing a motion track of a hand for the plurality of frames of consecutive hand images on a timeline; identifying the action of the purchaser as the take-up action or the put-back action according to a movement direction of the motion track of a hand relative to the shelf and acquiring a plurality of frames of consecutive hand images of the take-up action or the put-back action of items of the purchaser's hand of the purchaser in front of a shelf bearing the items, and establishing a motion track of a hand for the plurality of frames of consecutive hand images on a timeline; identifying the action of the purchaser as the take-up action or the put-back action according to a movement direction of the motion track of a hand relative to the shelf and characterized in that identifying the item at which the take-up action or the put-back action aims to acquire the item identification result in the step S2 specifically comprising: S21, acquiring a plurality of primary classification results, according to the acquired plurality of frames of hand images of the purchaser in front of the shelf bearing the items and a pre-trained first-level classification model, in which the pre-trained first-level classification model is a model that is constructed by an image identification technique of a convolutional neural network and trained by using data of all the items on the shelf; S22, acquiring a first-level classification result according to the plurality of primary classification results and a pre-trained first-level linear regression model; and S23, obtaining the item identification result according to the first-level classification result. S21, acquiring a plurality of primary classification results, according to the acquired plurality of frames of hand images of the purchaser in front of the shelf bearing the items and a pre-trained first-level classification model, in which the pre-trained first-level classification model is a model that is constructed by an image identification technique of a convolutional neural network and trained by using data of all the items on the shelf; S22, acquiring a first-level classification result according to the plurality of primary classification results and a pre-trained first-level linear regression model; and S23, obtaining the item identification result according to the first-level classification result.