Abstract: Embodiments of this application provide a text recommendation method performed at an electronic device. The method includes: extracting feature content of from the a target text; processing the feature content by using at least two text analysis models to obtain at least two semantic vectors; integrating the at least two semantic vectors into an integrated semantic vector of the target text; selecting, according to the integrated semantic vector and an integrated semantic vector of at least one to-be-recommended text, a recommended text corresponding to the target text from the at least one to-be-recommended text. Because the integrated semantic vector of the target text is obtained based on the at least two text analysis models, the integrated semantic vector has a stronger representing capability. When text recommendation is subsequently performed, an association degree between the recommended text and the target text can be increased, thereby improving recommendation accuracy.

Abstract: Embodiments of this application provide a text recommendation method performed at an electronic device. The method includes: extracting feature content of from the a target text; processing the feature content by using at least two text analysis models to obtain at least two semantic vectors; integrating the at least two semantic vectors into an integrated semantic vector of the target text; selecting, according to the integrated semantic vector and an integrated semantic vector of at least one to-be-recommended text, a recommended text corresponding to the target text from the at least one to-be-recommended text. Because the integrated semantic vector of the target text is obtained based on the at least two text analysis models, the integrated semantic vector has a stronger representing capability. When text recommendation is subsequently performed, an association degree between the recommended text and the target text can be increased, thereby improving recommendation accuracy.