Hierarchical text classification poses a significant challenge in natural language processing due to its intricate label hierarchy. Existing text classification methods often face dual constraints of efficiency and performance. To overcome these challenges, this study proposes a lightweight graph convolutional network model enhanced with jump connections (JumpLiteGCN). This significantly reduces the model’s complexity and computational costs by simplifying the network structure. Moreover, integrating jump connection mechanisms enhances the flow of information in deep networks, better capturing and utilizing hierarchical label information, thus significantly improving classification accuracy. In addition, we propose an adaptive loss function weight calculation method that computes the label weights based on hierarchical relationships and applies them to loss function, enabling the model to focus more on the accurate prediction of important samples during training, further enhancing the model’s performance and generalization ability. Extensive experiments conducted on two public hierarchical text classification datasets demonstrate that our method surpasses existing state-of-the-art approaches across multiple key performance metrics while significantly reducing the model’s training and inference times.