There are two problems in existing hierarchical text classification model: underutilization of the label information across hierarchical instances, and lack of handling unbalanced label distribution. To solve these problems, this study proposes a hierarchical text classification method for label co-occurrence and long-tail distribution (LC-LTD) to study the global semantic of text based on shared labels and balanced loss function for long-tail distribution. First, a contrastive learning objective based on shared labels is devised to narrow the semantic distance between text representations with more shared labels in feature space and to guide the model to generate discriminative semantic representations. Second, the distribution balanced loss function is introduced to replace binary cross-entropy loss to alleviate the long-tail distribution problem inherent in hierarchical classification, improving the generalization ability of the model. LC-LTD is compared with various mainstream models on WOS and BGC public datasets, and the results show that the proposed method achieves better classification performance and is more suitable for hierarchical text classification.