2. The method according to claim 1 , before the sending the global initial statistical information to the slave node, the method comprises: setting a different initial value for each hyper-parameter of the hierarchical Latent Dirichlet Allocation model; dividing the text set into multiple text subsets, wherein the number of the text subsets is the same as the number of nodes; and allocating one topic path for each document in the text set, allocating one topic for each word in the document, and according to the statistical total number of words in the text set, the total number of words contained in each document, and a word list of the text set, obtaining a document-topic count matrix and a topic-word count matrix.
1. A data processing method, comprising: sending, by a master node, global initial statistical information to a plurality of slave nodes, wherein the global initial statistical information comprises: text subset information divided in advance according to a text set, preset initial hyper-parameter information of a hierarchical Latent Dirichlet Allocation model, a pre-established nested Chinese restaurant process prior of the text set, hierarchical topic path information of a document, document-topic count matrix information, and topic-word count matrix information; receiving local statistical information from each of the plurality of slave nodes; merging the received local statistical information of each slave node, to obtain new global statistical information, wherein the local statistical information comprises: a document-topic count matrix, a topic-word count matrix and a document hierarchical topic path of each slave node, and the new global statistical information comprises: global text-topic count matrix information, topic-word count matrix information, topic-word count matrix information of each slave node, and a global document hierarchical topic path; after judging that a Gibbs sampling performed by a slave node has ended, calculating a probability distribution between the document and a topic and a probability distribution between the topic and a word according to the new global statistical information, wherein the Gibbs sampling is used to allocate a topic for each word of each document, and allocate a hierarchical topic path for each document; according to the probability distributions obtained through calculation, establishing a likelihood function of the text set, and maximizing the likelihood function, to obtain a new hierarchical Latent Dirichlet Allocation model hyper-parameter; and after judging that an iteration of solving for a hierarchical Latent Dirichlet Allocation model hyper-parameter has converged, and according to the new hierarchical Latent Dirichlet Allocation model hyper-parameter, calculating and outputting the probability distribution between the document and topic and the probability distribution between the topic and word.