5. A search server, comprising a processor; and a memory coupled to the processor and having multiple computer executable instructions stored thereon, the processor being configured to execute the computer executable to: receive a search request, wherein the search request carries: keyword information to be searched for, and an instant interest model of a user associated with the search request and a long-term interest model of the user which are acquired by a search application server, wherein: the instant interest model of the user is an instant interest model vector formed by score values of N dimensions, wherein: the score value of each of the N dimensions indicates an interest level of the user on said each of N dimensions, and is calculated according to relevant data of a query sequence q(1), . . . , q(t-1), q(t) in a same search context session initiated by the user, wherein: the same search context session corresponds to the search request, and the search context session comprises a sum of a period of preset time before the current query q(t) occurs and a time when the current query q(t) occurs; calculate a relevance score value of each member engine utilizing: the search request, meta index information describing stored contents of the member engine, the instant interest model, and the long-term interest model; and select one or more member engines to search for the keyword information according to the relevance score value.
5. A search server, comprising a processor; and a memory coupled to the processor and having multiple computer executable instructions stored thereon, the processor being configured to execute the computer executable to: receive a search request, wherein the search request carries: keyword information to be searched for, and an instant interest model of a user associated with the search request and a long-term interest model of the user which are acquired by a search application server, wherein: the instant interest model of the user is an instant interest model vector formed by score values of N dimensions, wherein: the score value of each of the N dimensions indicates an interest level of the user on said each of N dimensions, and is calculated according to relevant data of a query sequence q(1), . . . , q(t-1), q(t) in a same search context session initiated by the user, wherein: the same search context session corresponds to the search request, and the search context session comprises a sum of a period of preset time before the current query q(t) occurs and a time when the current query q(t) occurs; calculate a relevance score value of each member engine utilizing: the search request, meta index information describing stored contents of the member engine, the instant interest model, and the long-term interest model; and select one or more member engines to search for the keyword information according to the relevance score value. 6. The search server according to claim 5 , wherein the processor is further configured to: calculate a first maximum similarity between the keyword information and the meta index information of the member engine; calculate a second maximum similarity between the meta index information of the member engine and the instant interest model based on that a similarity between the meta index information of the member engine and the keyword information is greater than a first threshold value and a similarity between the meta index information of the member engine and the long-term interest model is greater than a second threshold value; calculate a third maximum similarity between the meta index information of the member engine and the long-term interest model based on that the similarity between the meta index information of the member engine and the keyword information is greater than a third threshold value and a similarity between the meta index information of the member engine and the instant interest model is greater than a fourth threshold value; and calculate a fourth maximum similarity between the meta index information of the member engine and a resulting vector of a weighted sum of the long-term interest model and the instant interest model based on that the similarity between the meta index information of the member engine and the keyword information is greater than a fifth threshold value; and calculate a similarity score value of the member engine according to the first maximum similarity, the second maximum similarity, the third maximum similarity, and the fourth maximum similarity.