Service discovery method based on clustering and Gaussian LDA

The invention discloses a service discovery method based on clustering and Gaussian LDA, and the method comprises the following steps: carrying out the data analysis of a service data set, and carrying out the training of paragraph embedding and word embedding through Doc2Vec and Word2Vec; clustering the Doc2Vec vector set by using a modified K-Means algorithm; performing extended query based on the word embedding vector set to obtain an extended query statement Qe and an extended query vector Vqe; calculating the average cosine similarity between the expanded query statement and the Doc2Vec matrix of each clustering cluster obtained by clustering based on the expanded query statement, and taking the cluster with the highest similarity as a target cluster; constructing a Gaussian LDA modelbased on the selected target cluster and the word embedding vector obtained by training to obtain document-topic distribution and Gaussian distribution of topics; and calculating the probability thateach service in the targe