8. The method according to claim 1 , wherein the meta index is statistical data of one or any combination of a database, and a sub-database corresponding to the member engine, a document or a record contained in the database, a document or a record contained in the sub-database, and a term contained in the document or the record.
8. The method according to claim 1 , wherein the meta index is statistical data of one or any combination of a database, and a sub-database corresponding to the member engine, a document or a record contained in the database, a document or a record contained in the sub-database, and a term contained in the document or the record. 10. The method according to claim 8 , wherein selecting the member engine according to the meta index of each member engine, the search request and the user interest model comprises: obtaining a first similarity of a first document in a certain database to a search request vector Q, wherein the first document satisfies a condition that the similarity of the first document to the search request vector Q is the highest; the search request vector Q=(q 1 , q 2 , . . . qi . . . qk), and qi is a weight of a term ti in the search request; obtaining a second similarity of a second document in the database to a user interest model R, wherein the second document satisfies a condition that the similarity of the second document to the user interest model vector R is the highest while a matching degree with a vector Q′ (q 1 ′, q 2 ′ . . . qm′) meets a specified threshold value, the vector Q′ is a conversion form of the search request vector Q specific to the user interest model vector R, the user interest model vector R=(r 1 , r 2 , . . . ri . . . , rn), and ri is a weight score of the i th dimension of the user interest model; selecting a higher value from the first similarity and the second similarity as a similarity of a combination of the search request and the user interest model to the database; repeating the above method, so as to obtain the similarity of the combination of the search request and the user interest model to each database, wherein each database is corresponding to a member engine; and ranking each database according to the similarity of the combination of the search request and the user interest model to each database, and selecting member engines corresponding to one or more databases having larger similarities ranked top.