Popular science text classification aims to classify the popular science articles according to the popular science classification system. Concerning the problem that the length of popular science articles often exceeds 1 000 words， which leads to the model hard to focus on key points and causes poor classification performance of the traditional models， a model for long text classification combining knowledge graph to perform two-level screening was proposed to reduce the interference of topic-irrelevant information and improve the performance of model classification. First， a four-step method was used to construct a knowledge graph for the domains of popular science. Then， this knowledge graph was used as a distance monitor to filter out irrelevant information through training sentence filters. Finally， the attention mechanism was used to further filter the information of the filtered sentence set， and the attention-based topic classification model was completed. Experimental results on the constructed Popular Science Classification Dataset （PSCD） show that the text classification algorithm model based on the domain knowledge graph information enhancement has higher F1-Score. Compared with the TextCNN model and the BERT （Bidirectional Encoder Representations from Transformers） model， the proposed model has the F1-Score increased by 2.88 percentage points and 1.88 percentage points respectively， verifying the effectiveness of knowledge graph to long text information screening.