A similarity measuring method improved through a collaborative filtering recommendation algorithm includes the following steps of (S1) building a rating matrix R(n*m) of n users in a user set U={U1, U2,..., Un} to m items in an item set I={I1, I2,..., Im}, taking Ra,i as representation of rating of an item Ii, wherein Ua belongs to U and Ii belongs to I, (S2) calculating the similarity sim(Ua, Ub) between a user Ua and a user Ub and the similarity sim(Ii, Ij) between an item Ii and an item Ij, defining a similarity influence divisor epsilon, so that sim'(Ua, Ub) equals to epsilon* sim(Ua, Ub) and sim'(Ii, Ij) equals to epsilon* sim'(Ii, Ij), (S3) taking a parameter lambada in an interval between 0 and 1, and predicting rating of the users to the items according to lambada, epsilon, an average rating value of the users to the items, similarity between the users and similarity between the items.