Text style transfer is always a hot spot in Natural Language Processing (NLP). In recent years, as the development of sequence generation methods, many researchers focus on style transfer on non-parallel corpora. Specifically, this task wants to change the style of the sentence while keeping the original content. To achieve this target, many works have been proposed which based on the generative adversarial network. But due to the instability of adversarial training and the limitation of the independence assumption between the style and semantic information, these methods are hard to learn an effective and efficient transfer model. In this study, motivated by statistic learning methods, a definition of the text style is given. The style of the corpus can be captured by the covariance matrix of its sentences’ semantic vectors. From this perspective, the text style is dependent on all the semantic information. We then propose a learning free transfer method where the only thing we need is a pre-trained auto-encoder to produce the semantic vectors. With a pair of matrix transformations, including whitening transformation and stylizing transformation, performing on these vectors, we achieve text style transfer.