A method for generating natural language description, applicable to an electronic device, characterized in that the method comprises: processing a media object by using a plurality of natural language description models, to obtain a plurality of first feature vectors corresponding to a plurality of feature types; fusing the plurality of first feature vectors to obtain a second feature vector; and generating natural language description for the media object according to the second feature vector, wherein the natural language description expresses the media object in natural language, wherein the media object comprises a plurality of blocks, each first feature vector in the plurality of first feature vectors comprises a plurality of fourth features in one-to-one correspondence with the plurality of blocks, and wherein the fusing the plurality of first feature vectors to obtain a second feature vector comprises: extracting a plurality of feature sets in a one-to-one correspondence with the plurality of blocks from the plurality of first feature vectors, wherein each feature set in the plurality of feature sets comprises a fourth feature corresponding to one of the plurality of blocks in the plurality of first feature vectors; obtaining an average feature of each feature set in the plurality of feature sets, to obtain a plurality of average features in one-to-one correspondence with the plurality of blocks; and determining a feature vector comprising the plurality of average features as the second feature vector. processing a media object by using a plurality of natural language description models, to obtain a plurality of first feature vectors corresponding to a plurality of feature types; fusing the plurality of first feature vectors to obtain a second feature vector; and generating natural language description for the media object according to the second feature vector, wherein the natural language description expresses the media object in natural language, wherein the media object comprises a plurality of blocks, each first feature vector in the plurality of first feature vectors comprises a plurality of fourth features in one-to-one correspondence with the plurality of blocks, and wherein the fusing the plurality of first feature vectors to obtain a second feature vector comprises: extracting a plurality of feature sets in a one-to-one correspondence with the plurality of blocks from the plurality of first feature vectors, wherein each feature set in the plurality of feature sets comprises a fourth feature corresponding to one of the plurality of blocks in the plurality of first feature vectors; obtaining an average feature of each feature set in the plurality of feature sets, to obtain a plurality of average features in one-to-one correspondence with the plurality of blocks; and determining a feature vector comprising the plurality of average features as the second feature vector. extracting a plurality of feature sets in a one-to-one correspondence with the plurality of blocks from the plurality of first feature vectors, wherein each feature set in the plurality of feature sets comprises a fourth feature corresponding to one of the plurality of blocks in the plurality of first feature vectors; obtaining an average feature of each feature set in the plurality of feature sets, to obtain a plurality of average features in one-to-one correspondence with the plurality of blocks; and determining a feature vector comprising the plurality of average features as the second feature vector. <EOS>