Weibo is one of the top social media platforms in China, similar to X/Twitter. There are many fake accounts on Weibo who may have various negative impacts on the platform and community. However, most existing work on fake user datasets and detection models is concentrated on X/Twitter, with little consideration given to Weibo. In this paper, we first review some methods for detecting fake accounts on social networking platforms. Secondly, we introduce a dataset called Weibo-FA dataset collected from the Weibo platform, which contains profile information of both fake and genuine accounts. Then Weibo-FA dataset is compared with a baseline dataset using several basic machine learning models and neural networks to evaluate its applicability in traditional models. Subsequently, the Weibo-FA dataset is employed to train two state-of-the-art neural network-based models to demonstrate its suitability for advanced models. The test results are compared with related work. Regarding the results, they indicate that the proposed Weibo-FA dataset exhibits good usability and completeness. It not only enhances the performance of traditional simple models but also achieves high accuracy in detecting fake accounts for advanced complex models.