3. A parameter inference calculation apparatus based on a Latent Dirichlet Allocation model, comprising: a receiver; a plurality of processors; and a memory unit implemented by hardware, wherein the plurality of processors comprise a master processor and one or more slave processors, wherein the receiver is configured to receive a document set, wherein the master processor is configured to: segment the document set into a plurality of document subsets; set an initial first hyperparameter, an initial second hyperparameter, the initial number of topics, an initial global count matrix of documents and topics, and an initial global count matrix of topics and words; send to each slave processor a document subset, the initial first hyperparameter, the initial second hyperparameter, the initial number of topics, the initial global count matrix of documents and topics, and the initial global count matrix of topics and words; receive from each slave processor a local count matrix of documents and topics and a local count matrix of topics and words, and perform merging to obtain a global count matrix of documents and topics and a global count matrix of topics and words; determine, for each slave processor, whether the slave processor solves the Latent Dirichlet Allocation model and updates the local count matrix of documents and topics and the local count matrix of topics and words converges, and when the process of solving the Latent Dirichlet Allocation model and updating the local count matrix of documents and topics and the local count matrix of topics and words does not converge, send the global count matrix of documents and topics and the global count matrix of topics and words to the slave processor for calculation, and continue to update the local count matrix of documents and topics and the local count matrix of topics and words until the process of calculating the Latent Dirichlet Allocation model and updating the local count matrix of documents and topics and the local count matrix of topics and words converges, and output the global count matrix of documents and topics and global count matrix of topics and words that are eventually obtained; obtain, through calculation, a probability distribution between documents and topics and a probability distribution between topics and words according to the global count matrix of documents and topics and the global count matrix of topics and words; obtain, by using an expectation maximization algorithm, a first hyperparameter, a second hyperparameter, and the number of topics that maximize log likelihood functions of the probability distributions; determine, for each slave processor, whether the first hyperparameter, the second hyperparameter, and the number of topics converge, and when the first hyperparameter, the second hyperparameter, and the number of topics do not converge, send the first hyperparameter, the second hyperparameter, and the number of topics to the slave processor until an optimal first hyperparameter, an optimal second hyperparameter, and the optimal number of topics that maximize the log likelihood functions of the probability distributions converge, and output the probability distributions, the optimal first hyperparameter, the optimal second hyperparameter, and the optimal number of topics that are eventually obtained, and wherein each slave processor is configured to: receive the document subset, the initial first hyperparameter, the initial second hyperparameter, the initial number of topics, the initial global count matrix of documents and topics, and the initial global count matrix of topics and words that are sent by the master processor, calculate the Latent Dirichlet Allocation model, update the local count matrix of documents and topics and the local count matrix of topics and words, and return the local count matrix of documents and topics and the local count matrix of topics and words to the master processor; receive the global count matrix of documents and topics, the global count matrix of topics and words, the first hyperparameter, the second hyperparameter, and the number of topics that are sent by the master processor and put them into the Latent Dirichlet Allocation model for local calculation, and wherein the memory unit is configured to store the document set, the initial first hyperparameter, the initial second hyperparameter, the initial number of topics, the initial global count matrix of documents and topics, the initial global count matrix of topics and words, the local count matrices of documents and topics, the local count matrices of topics and words, the global count matrix of documents and topics, and the global count matrix of topics and words.
4. The calculation apparatus according to claim 3 , wherein the master processor comprises: an initialization unit configured to: segment the document set into the plurality of document subsets; and set the initial first hyperparameter, the initial second hyperparameter, the initial number of topics, the initial global count matrix of documents and topics, and the initial global count matrix of topics and words; a parameter estimation unit configured to: construct the log likelihood functions of the probability distributions; obtain, by using the expectation maximization algorithm, the first hyperparameter, the second hyperparameter, and the number of topics that maximize the log likelihood functions of the probability distributions; determine whether the first hyperparameter, the second hyperparameter, and the number of topics converge, and when the first hyperparameter, the second hyperparameter, and the number of topics do not converge, send, through a first data transceiver unit, the first hyperparameter, the second hyperparameter, and the number of topics to the slave processor calculation apparatus, until the optimal first hyperparameter, the optimal second hyperparameter, and the optimal number of topics that maximize the log likelihood functions of the probability distributions converge, and output the probability distributions, the optimal first hyperparameter, the optimal second hyperparameter, and the optimal number of topics that are eventually obtained; a Latent Dirichlet Allocation model calculation unit configured to: merge a plurality of local count matrixes of documents and topics and local count matrixes of topics and words that are received through the first data transceiver unit to obtain the global count matrix of documents and topics and the global count matrix of topics and words; determine whether a process of solving, by each slave processor, the Latent Dirichlet Allocation model and updating the local count matrix of documents and topics and the local count matrix of topics and words converges, and when the process of solving the Latent Dirichlet Allocation model and updating the local count matrix of documents and topics and the local count matrix of topics and words does not converge, send the global count matrix of documents and topics and the global count matrix of topics and words to each slave processor for calculation, and continue to update the local count matrix of documents and topics and the local count matrix of topics and words until the process of calculating the Latent Dirichlet Allocation model and updating the local count matrix of documents and topics and the local count matrix of topics and words converges, and output the global count matrix of documents and topics and global count matrix of topics and words that are eventually obtained; and obtain, through calculation, the probability distribution between documents and topics and the probability distribution between topics and words according to the global count matrix of documents and topics and the global count matrix of topics and words; and the first data transceiver unit configured to: send each document subset of a plurality of document subsets, the initial first hyperparameter, the initial second hyperparameter, the initial number of topics, the initial global count matrix of documents and topics, and the initial global count matrix of topics and words to each slave processor correspondingly; receive the local count matrix of documents and topics and the local count matrix of topics and words that are returned by each slave processor; send the global count matrix of documents and topics and the global count matrix of topics and words to each slave processor; and send the first hyperparameter, the second hyperparameter, and the number of topics to each slave processor.