5. The method according to claim 4 , after establishing the likelihood function of the text set, and maximizing the likelihood function, to obtain the new hierarchical Latent Dirichlet Allocation model hyper-parameter, the method comprises: judging whether an iteration of solving for the hierarchical Latent Dirichlet Allocation model hyper-parameter has converged when the gradient of a likelihood function value of the text set corresponding to the hierarchical Latent Dirichlet Allocation model hyper-parameter is less than a preset gradient threshold.
4. The method according to claim 3 , further comprising: if the Gibbs sampling performed by the slave node does not end, sending the new global statistical information to the slave node.