Key sentence extraction technology refers to using artificial intelligence to automatically find key sentences from a long text. This technology can be used for preprocessing information retrieval and is of great significance for downstream tasks such as text classification and extractive summarization. Traditional unsupervised key sentence extraction technologies are mostly based on statistics and graphical model methods, which have problems such as low accuracy and the need to build a large-scale corpus in advance. This study proposes T5KSEChinese, a method that can extract key sentences without supervision in the Chinese context. This method uses an encoder-decoder architecture to ignore the mismatch in length between the target sentence and the original text by inputting and outputting prompt words to obtain more accurate results. At the same time, a contrastive learning positive sample construction method is also proposed and combined with contrastive learning to conduct semi-supervised training on the encoder part of the model, which can improve the performance of downstream tasks. The method uses lightweight models to outperform the large language model with tens of times the number of parameters in the unsupervised downstream task. The final experimental results prove the accuracy and reliability of the proposed method.