A medical image recognition method, the method comprising: by a first computer device, obtaining a to-be-recognized original medical image set, the to-be-recognized original medical image set comprising at least one to-be-recognized original medical image; obtaining meta information of each to-be-recognized original medical image in the to-be-recognized original medical image set, the meta information comprising information associated with the to-be-recognized original medical image; matching the to-be-recognized original medical image with a target medical image in a case that the meta information of the to-be-recognized original medical image satisfies a sample extraction condition, the target medical image being a preset image template; and determining, in a case that the to-be-recognized original medical image is successfully matched with the target medical image, that the to-be-recognized original medical image is a to-be-recognized medical image, until a to-be-recognized medical image set is obtained from the to-be-recognized original medical image set; the to-be-recognized medical image set comprising at least one to-be-recognized medical image; extracting a to-be-recognized area corresponding to each to-be-recognized medical image in the to-be-recognized medical image set, the to-be-recognized area being a part of the to-be-recognized medical image; and determining a recognition result of each to-be-recognized area through a medical image recognition model, the medical image recognition model being obtained through training according to a medical image sample set, the medical image sample set comprising at least one medical image sample, each medical image sample carrying corresponding annotation information, the annotation information being used for representing a type of the medical image sample, the recognition result being used for representing a type of the to-be-recognized medical image. by a first computer device, obtaining a to-be-recognized original medical image set, the to-be-recognized original medical image set comprising at least one to-be-recognized original medical image; obtaining meta information of each to-be-recognized original medical image in the to-be-recognized original medical image set, the meta information comprising information associated with the to-be-recognized original medical image; matching the to-be-recognized original medical image with a target medical image in a case that the meta information of the to-be-recognized original medical image satisfies a sample extraction condition, the target medical image being a preset image template; and determining, in a case that the to-be-recognized original medical image is successfully matched with the target medical image, that the to-be-recognized original medical image is a to-be-recognized medical image, until a to-be-recognized medical image set is obtained from the to-be-recognized original medical image set; the to-be-recognized medical image set comprising at least one to-be-recognized medical image; extracting a to-be-recognized area corresponding to each to-be-recognized medical image in the to-be-recognized medical image set, the to-be-recognized area being a part of the to-be-recognized medical image; and determining a recognition result of each to-be-recognized area through a medical image recognition model, the medical image recognition model being obtained through training according to a medical image sample set, the medical image sample set comprising at least one medical image sample, each medical image sample carrying corresponding annotation information, the annotation information being used for representing a type of the medical image sample, the recognition result being used for representing a type of the to-be-recognized medical image.