Text style transfer is one of the hot issues in the field of natural language processing in recent years. It aims to transfer the specific style or attributes of the text (such as emotion, tense, gender, etc.) through editing or generating while retaining the text content. The purpose of this study is to sort out the existing methods in order to advance this research field. First, the problem of text style transfer is defined and the challenges are given. Then, the existing methods are classified and reviewed, focusing on the TST methods based on unsupervised learning and further dividing them into the implicit methods and the explicit methods. The implementation mechanisms, advantages, limitations, and performance of each method are also analyzed. Subsequently, the performance of several representative methods on automatic evaluation indicators such as transfer accuracy, text content retention, and perplexity are compared through experiments. Finally, the research of text style transfer is concluded and prospected.