Abstract: Embodiments of this application provide a text backup method and apparatus, and device, and a computer-readable storage medium. The method includes performing statistical feature extraction on a text to be analyzed, to obtain a statistical feature vector of the text to be analyzed; performing semantic feature extraction on the text to be analyzed, to obtain a semantic feature vector of the text to be analyzed; performing at least two times of fusion processing on the statistical feature vector and the semantic feature vector, to obtain a probability value corresponding to the text to be analyzed; determining the text to be analyzed as a text to be backed up when the probability value is greater than a threshold; and backing up the text to be backed up.

Abstract: A method and apparatus for recalling news based on artificial intelligence, a device and a storage medium. The method comprises: building an index repository according to candidate news, the index repository including M search trees, each search tree being a complete binary tree including at least two layers, each non-leaf node in each search tree corresponding to a semantic index vector, each piece of candidate news corresponding to a leaf node in each search tree; when news needs to be recommended to the user, generating a user's semantic index vector according to the user's interest tag; with respect to each search tree, respectively according to semantic index vectors corresponding to non-leaf nodes therein and the user's semantic index vector, determining a path from a first layer of non-leaf nodes to a leaf node, and regarding candidate news corresponding to the leaf node on the path as a recall result.

Abstract: A method and apparatus for recalling news based on artificial intelligence, a device and a storage medium. The method comprises: building an index repository according to candidate news, the index repository including M search trees, each search tree being a complete binary tree including at least two layers, each non-leaf node in each search tree corresponding to a semantic index vector, each piece of candidate news corresponding to a leaf node in each search tree; when news needs to be recommended to the user, generating a user's semantic index vector according to the user's interest tag; with respect to each search tree, respectively according to semantic index vectors corresponding to non-leaf nodes therein and the user's semantic index vector, determining a path from a first layer of non-leaf nodes to a leaf node, and regarding candidate news corresponding to the leaf node on the path as a recall result.