A semantic extraction method based on a natural language is provided. In a character library based on a single-character granularity, a character of an input natural language is queried, to obtain first semantic vectors representing semantic descriptions of the character. Candidate semantic intentions corresponding to second semantic vectors and weight vectors corresponding to the candidate semantic intentions are obtained, wherein the second semantic vectors are obtained by using a neural network and correspond to different permutations and combinations of the first semantic vectors. A candidate semantic intention is selected as a final semantic intention of the natural language based on a preset condition, and key information is obtained corresponding to the final semantic intention according to a weight vector corresponding to the final semantic intention.