The invention discloses a method for mining a frequent item set based on an effective weighted tree, comprising the following steps: S1, reading a database D from a network, the database D comprisingN transactions, each transaction comprising different items, the number of items and the weight w occupied by each item; S2, calculating a transaction weight tw of each transaction in the database D,and generating a transaction weight table; S3, calculating the weight W of the item set S, and presetting a threshold value minws, wherein if W is greater than or equal to minws, sthe item set S is afrequent item set; if W < minws, the item set S is a non-frequent item set; and S4, constructing an effective weighted tree model for mining the frequent item set. According to the method, weights aregiven to the item sets, weight calculation is carried out, so that an effective weighted tree model is constructed, the mining efficiency of the frequent item sets is improved, memory application isreduced, and the method is suitable for a database of big data.