1. A search method, comprising: receiving a search request from a search client; extracting a user interest model from user personalized data according to the search request; obtaining a meta index of each member engine; selecting a member engine according to the meta index of each member engine, the search request, and the user interest model; and sending the search request to the selected member engine, so as to enable the selected member engine to complete searching; wherein the user interest model is a vector formed with scores given to each of several interest dimensions denoting user interests; the user interest model comprises a static interest model and a dynamic interest model: the static interest model is obtained by obtaining frequencies of words belonging to a certain interest dimension in a static user profile of a user, calculating a sum of the frequencies of the words belonging to the interest dimension as a score of the interest dimension, and forming a score vector with different scores to create a static interest model; and the dynamic interest model is obtained by obtaining frequencies of words belonging to a certain interest dimension in a document clicked in a search history of a user, calculating a sum of the frequencies of the words belonging to the interest dimension in the document as a score specific to the interest dimension in the document, forming a score vector specific to the document with different scores specific to different interest dimensions, and a sum of the score vectors specific to different documents to create a dynamic interest model.
6. The method according to claim 1 , further comprising: forwardly weighing the score vector specific to the document when an evaluation on the clicked document by the user is good; inversely weighing the score vector specific to the document when an evaluation on the clicked document by the user is not good; or descending the score vector specific to the document with time lapsed after the document is clicked.