1. A music recommendation method for use with a music recommendation apparatus, the method comprising: obtaining a music belongingness function of a first piece of music, the music belongingness function of music comprising a first set granularities of music in different dimensions, wherein the dimensions are classifications of music and the granularities are classifications of the dimensions, and an expression of the music belongingness function being A(music, P k )={p kj |j=1, 2,. .. , n}, k=1, 2,. .. m, wherein P k represents a dimension, p kj represents a granularity, m represents a total number of the dimension and n represents a total number of the granularity in the dimension; obtaining a user belongingness function of a user, the user belongingness function comprising a second set of granularities indicating user music tastes in the different dimensions, and an expression of the user belongingness function being A(user, P k )={p ki |i=1, 2. .. , n}, k=1, 2,. .. m, wherein P k represents a dimension, p ki is a granularity, m represents a total number of the dimension and n represents a total number of the granularity in the dimension; calculating a granularity correlation function by using the music belongingness function and the user belongingness function, and an expression of the granularity correlation function being f ⁡ ( user , music , P k ) = { 0 ; when ⁢ ⁢ A ⁡ ( music , P k ) ∉ A ⁡ ( user , P k ) 1 ; when ⁢ ⁢ A ⁡ ( music , P k ) ∈ A ⁡ ( user , P k ) ; calculating a value of a probability function indicating user music tastes by using the granularity correlation function and a dimension weighting coefficient, and the probability function indicating user music tastes being expressed as F ⁡ ( user , music ) = ∑ k = 1 m ⁢ ⁢ ( α k · f ⁡ ( user , music , P k ) ) , wherein α k is the dimension weighting coefficient; and recommending the first piece of music to the user when the value of the probability function indicating user music tastes for the first piece of music is greater than a preset threshold; wherein the obtaining the user belongingness function comprises: getting a behavior function indicating user music tastes according to a behavior that the user takes to a second piece of music, and an expression of the behavior function indicating user music tastes being expressed as F ′ ⁡ ( user , music ) = ∑ k = 1 y ⁢ ⁢ ( q k · Q k ⁡ ( user , music ) ) , wherein Q k (user, music) represents a result value of a Q k behavior that the user takes to the second piece of music; q k represents a behavior weighting coefficient and y represents a total number of kinds of behavior; calculating an acquaintance function between the user and a third set of granularities by using the behavior function, and an expression of the acquaintance function being f ′ ⁡ ( user , p ki ) = ∑ j = 1 x ⁢ ⁢ F ′ ⁡ ( user , m j ) , wherein m j represents a third piece of music which belongs to the granularity p ki and x represents a total number of the third pieces of music which belongs to the granularity p ki ; and using a fourth set of granularities corresponding to top z values of descending values of the acquaintance function between the user and the third set of granularities in each dimension as granularities in z user belongingness functions respectively to form z user belongingness functions, z≧1.