1. A recommendation method using rough-set and multiple features mining integrally, said method comprising a training session and a prediction session, said training session building association rules, user clusters and rating tables, said training session comprising steps of: (a) providing data including user profiles, user rating logs and item contents; (b) pre-processing said data to obtain a transaction table; (c) associating transactions in said transaction table to obtain a plurality of associations to further obtain a plurality of association rules through data mining to be saved in an association rule database; (d) obtaining said user rating logs to divide users in said user rating logs into user clusters through a clustering algorithm to be saved in a user cluster database; and (e) analyzing said transactions in said transaction table to re-symbolize items into item categories through a statistical analysis and reorganizing user rating logs to obtain rating averages of said item categories and to further obtain a rating table of said re-symbolized item categories, said prediction session applying rough-set and statistical analysis prediction to obtain predicted rating values from said user rating logs, said prediction session comprising steps of: (f) finding a user cluster of related users to a target user from said user clusters to obtain a rating table of said related users and said target user; (g) based on said association rules, predicting unknown values in said rating table other than rating value of a target item of said target user to obtain a complete sub-matrix; (h) obtaining a class item, a referred item and a plurality of item sets in said sub-matrix, obtaining a plurality of first elementary sets by dividing said users with said class item, obtaining a plurality of second elementary sets by dividing said users with said item sets, and comparing said first elementary sets and said second elementary sets to obtain a lower approximation through a rough-set algorithm using a user cardinality constraint and an item cardinality constraint to further obtain a predicted rating value of said target item of said target user; (i) obtaining predicted rating values of said item categories in said rating table obtained through said statistical analysis prediction in said training session to further obtain another predicted rating value of said target item of said target user; and (j) obtaining a final predicted rating value of said target item of said target user through a switch-based mixing, wherein a first standard deviation is pre-set as a threshold; wherein said predicted rating value obtained through said statistical analysis prediction is obtained as said final predicted rating value of said target item on obtaining a second standard deviation bigger than said threshold, said second standard deviation being a standard deviation of past rating values of the same item category as that of said target item; and wherein said predicted rating value obtained through said rough-set algorithm is obtained as said final predicted rating value on obtaining said second standard deviation not bigger than said threshold.