The present invention discloses a word vector generation method based on the Gaussian distribution. The method comprises: firstly, preprocessing the corpus; secondly, using the punctuation to performtext division on the corpus; then combining the local and global information to infer the word meaning, and determining the mapping relationship between the word and the word meaning; and finally, obtaining a word vector by optimizing the objective function. The innovations and beneficial effects of the technical scheme of the present invention are as follows that: 1, words are represented based on the Gaussian distribution, point estimation characteristics of traditional word vectors are avoided, and more abundant information such as probabilistic quality, meaning connotation, an inclusion relationship, and the like can be brought to the word vectors; 2, multiple Gaussian distributions are used to represent the words, so that the linguistic characteristics of a word in the natural language can be coopered with; and 3, the similarity between the Gaussian distributions is defined based on the Hellinger distance, and by combining parameter updating and word meaning discrimination, the number of word meanings can be inferred adaptively, and the problem that the number of hypothetical word meanings of the model in the prior art is fixed is solved.