The invention relates to an unsupervised text similarity calculation method, which comprises the following steps of: 1, pre-training an embedded layer model, and pre-training all words in a
problem set to generate word vectors meeting the requirements of the model; 2, mining
semantic information of sentences through a coding layer network; step 3, performing model improvement based on TFIDF fusion; the method comprises the steps that when each question
sentence is input into a neural network, TFIDF calculation is conducted on each input question
sentence, calculated weights are input into theneural network, final
sentence vector representation is controlled, a normalized TFIDF calculation method is adopted, and the final sentence vector representation is fused into a coding layer and a representation layer. According to the method, the deep neural
network model (Bi-LSTM) is used for unsupervised training of the corpus to obtain the
language model, and the information of the large-scale corpus can be fully utilized in an unsupervised training mode, so that the
text matching accuracy is improved, and the
information retrieval precision is improved.