The present disclosure includes acquiring a keyword of information to be replied to, as a first feature, and acquiring a keyword of a pending reply in a pending reply set as a second feature, calculating, according to a correlation between the first feature and the second feature, a match between the information to be replied to and the pending reply, where the correlation between the first feature and the second feature is obtained through multiple trainings according to an original text and a reply to the original text that are acquired from a corpus environment, where the corpus environment includes a microblog, a forum, and a post bar, repeating the foregoing steps, until matches between the information to be replied to and all pending replies are obtained, and selecting a best matched pending reply as a reply to the information to be replied to.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for acquiring a feature correlation, comprising the following steps: acquiring, from a corpus environment, an original text and an eligible reply to the original text, wherein the corpus environment comprises at least one of a microblog, a forum, and a post bar, and wherein the eligible reply is a reply complying with a set condition; acquiring a keyword of the original text as a first feature; acquiring a keyword of the eligible reply as a second feature; and training a neural network model using the first feature and the second feature to obtain a correlation between the first feature and the second feature. 2. The method according to claim 1, wherein acquiring, from a corpus environment, an original text and an eligible reply to the original text, comprises: acquiring, from the corpus environment, the original text and a reply to the original text; and cleaning the reply to the original text according to the set condition to obtain the eligible reply to the original text, wherein the set condition comprises that a count of words exceeds 5, that there be no attachment, and that the reply is within the first one hundred replies sorted in reply order. 3. An apparatus for acquiring a feature correlation, comprising: a receiver configured to acquire, from a corpus environment, an original text and an eligible reply to the original text, wherein the corpus environment comprises at least one of a microblog, a forum, and a post bar, and wherein the eligible reply is a reply complying with a set condition; and a processor coupled to the receiver and configured to: acquire a keyword of the original text as a first feature; acquire a keyword of the eligible reply as a second feature; and train a neural network model using the first feature and the second feature to obtain a correlation between the first feature and the second feature. 4. The apparatus according to claim 3, wherein the receiver is further configured to acquire, from the corpus environment, the original text and a reply to the original text, and wherein the processor is further configured to clean the reply to the original text according to the set condition to obtain the eligible reply to the original text, wherein the set condition comprises that a count of words exceeds 5, that there be no attachment, and that the reply is within the first one hundred replies sorted in reply order. 5. A method for automatically replying to information, comprising: receiving information to be replied to; acquiring a keyword of the information to be replied to as a first feature; acquiring a keyword of a pending reply in a pending reply set as a second feature; calculating, according to a correlation between the first feature and the second feature, a match between the information to be replied to and the pending reply, wherein the correlation between the first feature and the second feature is obtained through multiple trainings according to an original text and a reply to the original text that are acquired from a corpus environment, wherein the corpus environment comprises at least one of a microblog, a forum, and a post bar; repeating the steps of acquiring a first feature, acquiring a second feature, and calculating a match, until matches between the information to be replied to and all pending replies are obtained; and selecting a best matched pending reply as a reply to the information to implement an automatic reply to the information to be replied to. 6. The method according to claim 5, wherein the method further comprises: acquiring, from the corpus environment, the original text and an eligible reply to the original text, and wherein the eligible reply is a reply complying with a set condition; and training a neural network model using the first feature and the second feature to obtain the correlation between the first feature and the second feature. 7. The method according to claim 5, wherein after selecting the best matched pending reply as the reply to the information, the method further comprises performing customized processing on the best matched pending reply to obtain a customized reply. 8. The method according to claim 5, wherein acquiring the keyword of the pending reply in the pending reply set as the second feature comprises quickly retrieving replies in a reply database to obtain the pending reply set. 9. The method according to claim 5, wherein calculating, according to the correlation between the first feature and the second feature, the match between the information to be replied to and the pending reply, comprises calculating, according to P = ∑ i ∈ N   a i  x i , the match between the information to be replied to and the pending reply, wherein P is the match, N is an association set of the first feature and the second feature, i is an element in N, ai is a weight, and xi is the correlation between the first feature and the second feature. 10. An apparatus for automatically replying to information, comprising: a receiver configured to receive information to be replied to; a processor coupled to the receiver and configured to: acquire a keyword of the information to be replied to as a first feature; acquire a keyword of a pending reply in a pending reply set as a second feature; calculate, according to a correlation between the first feature and the second feature, a match between the information to be replied to and the pending reply until matches between the information to be replied to and all pending replies are obtained, wherein the correlation between the first feature and the second feature is obtained through multiple trainings according to an original text and a reply to the original text that are acquired from a corpus environment, wherein the corpus environment comprises at least one of a microblog, a forum, and a post bar, wherein the match calculating module sends the matches to the selecting module; and select a best matched pending reply as a reply to the information to implement an automatic reply to the information to be replied to. 11. The apparatus according to claim 10, wherein the receiver is further configured to acquire, from the corpus environment, the original text and an eligible reply to the original text, and wherein the processor is furtherconfigured to train a neural network model using the first feature and the second feature, to obtain the correlation between the first feature and the second feature. 12. The apparatus according to claim 10, wherein the processor is further configured perform customized processing on the best matched pending reply to obtain a customized reply. 13. The apparatus according to claim 10, wherein the processor is further configured to quickly retrieve replies in a reply database to obtain the pending reply set. 14. The apparatus according to claim 10, wherein the processor is further configured to calculate, according to P = ∑ i ∈ N   a i  x i , the match between the information to be replied to and the pending reply, wherein P is the match, N is an association set of the first feature and the second feature, i is an element in N, ai is a weight, and xi is the correlation between the first feature and the second feature.