1. A method for enriching contents of a current website, the method comprising: obtaining a corpus from the current website and other websites, and extracting object features from the corpus, wherein the corpus comprises specifications of the object and user reviews about the object; according to the corpus, constructing multi-dimensional vectors for the extracted features; for a specific feature, making similarity comparison of its multi-dimensional vector and multi-dimensional vectors of other extracted features; and determining features with similarities being higher than a predetermined threshold as a same feature, and reinforcing the current website with at least one feature which is different from those on the website and corresponding attributes of the at least one feature; wherein for a specific feature, comparing the similarity of its multi-dimensional vectors and multi-dimensional vectors of other extracted features comprises: aligning dimensions of multi-dimensional vectors of all extracted features, wherein for a specific feature, dimensions of multi-dimensional vectors of other features are treated as dimensions of the specific feature, so that each feature has the same number of dimensions; calculating mutual information between the specific feature in the corpus and each dimension of its multi-dimensional vectors as a weight of each dimension; and calculating similarities between multi-dimensional vectors of various features according to the weights, wherein similarities between multi-dimensional vectors of various features are calculated based on Euclidean distance between the multi-dimensional vectors.
8. The method according to claim 1 , wherein calculating mutual information between a specific feature in the corpus and each dimension of its multi-dimensional vectors as a weight of each dimension comprises: for dimensions added by considering dimensions of multi-dimension vectors of other features as dimensions of the specific feature, setting their weights to 0, otherwise calculating mutual information between the specific feature and a specific dimension of the feature as a weight of the dimension according to the following equation: Weight ⁢ ⁢ ( fj ; pf ) = log ⁢ ⁢ 2 ⁢ P ⁡ ( f j , p f ) P ⁡ ( f j ) ⁢ P ⁡ ( p f ) wherein, p f represents a specific feature, f j represents the j-th dimension of the specific feature, P(f j ,p f ) is the probability of the concurrent occurrence in the corpus of the specific feature and the j-th dimension in a sentence, P(f j ) is the occurrence probability in the corpus of the j-th dimension in a sentence, and P(p j ) is the occurrence probability in the corpus of the specific feature in a sentence.