FastText is a precise and efficient text classification model, but the precision is low when it is directly applied to Chinese long text classification. Regarding this problem, this study proposes a FastText method for Chinese long text classification, which combines TextRank key clause extraction with Term Frequency-Inverse Document Frequency (TF-IDF). Firstly, TextRank is used to extract the key clauses of the text as input features. Secondly, key words of the text are extracted by TF-IDF as a feature supplement. Finally, the extracted text features are input into the FastText model, which can preserve the key features of the target text while reducing the training corpus. The experimental results show that the accuracy of the proposed method on the datasets is 86.1%, which is about 4% higher than the classic FastText model.