A deep neural network to which data category information is added is established locally, to-be-identified data is input to an input layer of the deep neural network generated based on the foregoing data category information, and information of a category to which the to-be-identified data belongs is acquired, where the information of the category is output by an output layer of the deep neural network. A deep neural network is established based on data category information, such that category information of to-be-identified data is conveniently and rapidly obtained using the deep neural network, thereby implementing a category identification function of the deep neural network, and facilitating discovery of an underlying law of the to-be-identified data according to the category information of the to-be-identified data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data category identification method based on a deep neural network, comprising: establishing an initial deep neural network; generating a linear category analysis function after data category information is added to a locally saved initial linear category analysis function according to an input training sample vector set; acquiring an optimization function of the initial deep neural network according to a locally saved unsupervised auto-encoding model optimization function and the linear category analysis function; acquiring a parameter of the initial deep neural network according to the optimization function of the initial deep neural network; establishing the deep neural network according to a locally saved classification neural network, the initial deep neural network, and the parameter of the initial deep neural network, wherein the deep neural network has a multi-layer network structure comprising at least an input layer and an output layer; inputting to-be-identified data to the input layer of the deep neural network; and acquiring information of a category to which the to-be-identified data belongs, wherein the information of the category is output by the output layer of the deep neural network. 2. The method according to claim 1, wherein generating the linear category analysis function after data category information is added to the locally saved initial linear category analysis function according to the input training sample vector set comprises: performing relaxation processing on the initial linear category analysis function using a relaxation algorithm; performing normalization processing on the input training sample vector set to generate a normalized training sample vector set; and substituting the normalized training sample vector set into the initial linear category analysis function on which relaxation processing has been performed in order to generate the linear category analysis function. 3. The method according to claim 2, wherein the linear category analysis function is: ζ lda  ( W ) = ∑ k = 1 K  { ∑ ( x i , x j ∈ M )  w k T  x i  x j T  w k - ∑ ( x i , x j ∈ C )  w k T  x i  x j T  w k } wherein ζlda(W) is the linear category analysis function, wherein W is a parameter of the deep neural network, wherein W is a matrix comprising multiple elements, and the matrix is obtained by learning the normalized training sample vector set, wherein wk is any column vector in the matrix W, wherein wTk is a transposition of the column vector wk, wherein both xi and xj are training sample vectors in the normalized training sample vector set, wherein XjT is a transposition of xj, wherein M is a vector pair set comprising at least one pair of training sample vectors that belong to different categories, wherein C is a vector pair set comprising at least one pair of training sample vectors that belong to a same category, wherein (xi,xj)εM indicates that xi and xj belong to different categories, wherein (xi,xj)εC indicates that xi and xj belong to a same category, and wherein K is the total number of column vectors in the matrix W. 4. The method according to claim 1, wherein the optimization function of the initial deep neural network is: ζ=αζae(W)+(1−α)ζlda(W) wherein α is a coefficient of the optimization function of the initial deep neural network, and is preset and acquired according to an application scenario, wherein ζae(W) is the unsupervised auto-encoding model optimization function, wherein ζlda(W) is the linear category analysis function, and wherein ζ is the optimization function of the initial deep neural network. 5. The method according to claim 1, wherein acquiring the parameter of the initial deep neural network according to the optimization function of the initial deep neural network comprises: acquiring, according to the optimization function of the initial deep neural network and using a backpropagation algorithm, a gradient corresponding to the optimization function of the initial deep neural network; and acquiring, using a gradient descent algorithm or a quasi-Newton algorithm, the parameter of the initial deep neural network according to the gradient corresponding to the optimization function of the initial deep neural network. 6. The method according to claim 1, wherein establishing the deep neural network according to the locally saved classification neural network, the initial deep neural network, and the parameter of the initial deep neural network comprises: superimposing the classification neural network onto the initial deep neural network in order to generate an initial deep neural network that is obtained after superimposition processing; and establishing, using a backpropagation algorithm, the deep neural network according to the parameter of the initial deep neural network and the initial deep neural network that is obtained after superimposition processing. 7. A data category identification apparatus based on a deep neural network, comprising: a first establishing unit configured to establish an initial deep neural network; a generating unit configured to generate a linear category analysis function after data category information is added to a locally saved initial linear category analysis function according to an input training sample vector set; an optimization function acquiring unit configured to acquire an optimization function of the initial deep neural network according to a locally saved unsupervised auto-encoding model optimization function and the linear category analysis function; a parameter acquiring unit configured to acquire a parameter of the initial deep neural network according to the optimization function of the initial deep neural network; a second establishing unit configured to establish the deep neural network according to a locally saved classification neural network, the initial deep neural network, and the parameter of the initial deep neural network, wherein the deep neural network has a multi-layer network structure comprising at least an input layer and an output layer; and a data category identifying unit configured to: input to-be-identified data to the input layer of the deep neural network; and acquire information of a category to which the to-be-identified data belongs, wherein the information of the category is output by the output layer of the deep neural network. 8. The apparatus according to claim 7, wherein the generating unit is configured to: perform relaxation processing on the initial linear category analysis function using a relaxation algorithm; perform normalization processing on the input training sample vector set; and substitute the training sample vector set on which normalization processing has been performed into the initial linear category analysis function on which relaxation processing has been performed in order to generate the linear category analysis function. 9. The apparatus according to claim 8, wherein the linear category analysis function generated by the generating unit is: ζ lda  ( W ) = ∑ k = 1 K  { ∑ ( x i , x j ∈ M )  w k T  x i  x j T  w k - ∑ ( x i , x j ∈ C )  w k T  x i  x j T  w k } wherein ζlda(W) is the linear category analysis function, wherein W is a parameter of the deep neural network, wherein W is a matrix comprising multiple elements, and the matrix is obtained by learning the training sample vector set on which normalization processing has been performed, wherein wk is any column vector in the matrix W, wherein wTk is a transposition of the column vector wk, wherein both xi and xj are training sample vectors in the training sample vector set on which normalization processing has been performed, wherein xjT is a transposition of xj, wherein M is a vector pair set comprising at least one pair of training sample vectors that belong to different categories, wherein C is a vector pair set comprising at least one pair of training sample vectors that belong to a same category wherein (xi,xj)εM indicates that xi and xj belong to different categories, wherein (xi,xj)εC indicates that xi and xj belong to a same category, and wherein K is the total number of column vectors comprised in the matrix W. 10. The apparatus according to claim 7, wherein the optimization function, which is acquired by the optimization function acquiring unit, of the initial deep neural network is: ζ=αζae(W)+(1−α)ζlda(W) wherein α is a coefficient of the optimization function of the initial deep neural network, and is preset and acquired according to an application scenario, wherein ζae(W) is the unsupervised auto-encoding model optimization function, wherein ζlda(W) is the linear category analysis function, and wherein ζ is the optimization function of the initial deep neural network. 11. The apparatus according to claim 7, wherein the parameter acquiring unit is configured to: acquire, according to the optimization function of the initial deep neural network and using a backpropagation algorithm, a gradient corresponding to the optimization function of the initial deep neural network; and acquire, using a gradient descent algorithm or a quasi-Newton algorithm, the parameter of the initial deep neural network according to the gradient corresponding to the optimization function of the initial deep neural network. 12. The apparatus according to claim 7, wherein the second establishing unit is configured to: superimpose the classification neural network onto the initial deep neural network to generate an initial deep neural network that is obtained after superimposition processing, and establish, using a backpropagation algorithm, the deep neural network according to the parameter of the initial deep neural network and the initial deep neural network that is obtained after superimposition processing. 13. A non-transitory computer readable medium storing codes which, when executed by a processor of a network system, perform steps of: establishing an initial deep neural network; generating a linear category analysis function after data category information is added to a locally saved initial linear category analysis function according to an input training sample vector set; acquiring an optimization function of the initial deep neural network according to a locally saved unsupervised auto-encoding model optimization function and the linear category analysis function; acquiring a parameter of the initial deep neural network according to the optimization function of the initial deep neural network; establishing a deep neural network according to a locally saved classification neural network, the initial deep neural network, and the parameter of the initial deep neural network, wherein the deep neural network has a multi-layer network structure comprising at least an input layer and an output layer; inputting to-be-identified data to the input layer of the deep neural network; and acquiring information of a category to which the to-be-identified data belongs, wherein the information of the category is output by the output layer of the deep neural network. 14. The non-transitory computer readable medium according to claim 13, wherein generating the linear category analysis function after data category information is added to the locally saved initial linear category analysis function according to the input training sample vector set comprises: performing relaxation processing on the initial linear category analysis function using a relaxation algorithm, and performing normalization processing on the input training sample vector set to generate a normalized training sample vector set; and substituting the normalized training sample vector set into the initial linear category analysis function on which relaxation processing has been performed in order to generate the linear category analysis function.