Abstract: In various tasks in the field of natural language processing, models are widely gender biased. However, there is no relevant dataset for Chinese gender bias assessment and debiasing. According to 16 pairs of gender appellations, this paper screened out gender-unbiased sentences from a print media corpus, and constructed a Chinese sentence-level gender-unbiased data set SlguSet(sentence-level gender unbiased dataset) containing 20,000 sentences. This paper further proposes an index that can measure the degree of gender bias in pre-trained language models, and evaluates the gender bias in five popular pre-trained language models. The results show that there are different degrees of gender bias in the Chinese pre-training language model, and the dataset constructed in this article can effectively evaluate the gender bias in the Chinese pre-training language model.