Text classification is regarded as a crucial task in Natural Language Processing （NLP） field， with multi-label classification becoming a challenge due to large label space. To address this issue， a multi-label classification method integrating external semantic knowledge was proposed， named HSGIN（Heterogeneous Semantic Gated Interaction Network）， using values markers in children’s books as a case study. Firstly， text features were extracted through SBERT （Sentence Embeddings from Siamese BERT （Bidirectional Encoder Representations from Transformers）） and Bidirectional Long Short-Term Memory （Bi-LSTM） network. Then， entities and relations in the Knowledge Graph （KG） were modeled jointly using a Heterogeneous Graph Transformer （HGT）， and label features were extracted using the prior knowledge and semantic associations. Finally， the attention mechanism was employed to fuse text features and label features， generating distinct label feature representations. These embeddings were fed into a Gated Graph Neural Network （GGNN） to capture semantic dependencies and interaction patterns among labels for prediction. Experimental results show that compared with the existing state-of-the-art comparison method BERT， the proposed method achieves increases of 2.66， 0.47， and 1.16 percentage points in precision， recall， and F1 score， respectively. The above experimental results verify the effectiveness of the proposed method. At the same time， precise analysis of values markers in children’s books helps choose healthy books for children.