1. A semantic similarity evaluation method, comprising: performing word vectorization processing separately on words in a first sentence and a word in a second sentence to obtain a first word vector and a second word vector, wherein the first sentence comprises at least two words, wherein the first word vector comprises word vectors of all words in the first sentence, wherein the second sentence comprises at least one word, and wherein the second word vector comprises word vectors of all words in the second sentence; performing, in a preset word vector compression order, compression coding processing on the first word vector according to a first compression coding parameter to obtain a first statement vector; using, when the second sentence comprises one word, the second word vector as a second statement vector; performing, in the preset word vector compression order when the second sentence comprises at least two words, compression coding processing on the second word vector according to a second compression coding parameter to obtain a second statement vector; determining a vector distance between the first statement vector and the second statement vector by obtaining, by calculating a formula sim ⁡ ( υ 1 , υ 2 ) = ( υ 1 - υ 2 ) ⁢ ( υ 1 - υ 2 )  υ 1 * υ 2  , the vector distance between the first statement vector and the second statement vector, wherein υ 1 is the first statement vector, υ 2 is the second statement vector, and sim(υ 1 ,υ 2 ) is the vector distance; evaluating a semantic similarity between the first sentence and the second sentence according to the vector distance; and providing a translated text with the first sentence to a user when the semantic similarity meets a preset condition.