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.