The design of rock tunnels using drilling and blasting methods involves a multitude of interdisciplinary domains and exhibits a complex hierarchy of concepts. The related textual content spans diverse categories, presenting a vast amount of information with a rich and complex hierarchical structure, thereby escalating the difficulty in organizing knowledge. This study adopted an improved Latent Dirichlet Allocation (LDA) model to integrate and classify knowledge in the realm of tunnel support design. Considering the characteristics of relevant texts in the field of support design, the improved LDA model considering the semantic weights of words was proposed. Topic mining results show that under the semantically weighted LDA model, the model has higher consistency and lower confusion when the number of topics is between 10 and 14, so this study sets the number of text topics as 12. In addition, the consistency and confusion performance of the LDA model considering semantic weighting is better than that of the LDA model without semantic weighting. This study contributes to a more profound understanding of the professional terminology and concepts within the tunnel support design domain and aids in downstream tasks such as constructing domain ontologies.