Aiming at the multi-type classification problem and the underutilization of text semantics and labeling semantics in the vulnerability text classification task, a multi-label vulnerability classification method based on knowledge enhancement and labeling semantics (KELS) was proposed. Firstly, vulnerability attribute knowledge was introduced for multiple vulnerability types, and a strategy for effective integration of heterogeneous information was proposed to enhance the expressive ability of vulnerability text representation for multi-tag relationship, and further realize the enhancement of knowledge on text semantics through the deep fusion of Transformer encoder. Secondly, label embedding was introduced to propose a label attention mechanism that fuses knowledge-enhanced text representation with label embedding to capture semantic associations between text and labels for vulnerability classification. The experimental results show that comparing with the baseline model, the KELS model improves 0.98%, 0.32%, 2.31%, and 0.75% in Micro-P, Micro-F₁, Macro-P, and Macro-F₁, respectively, which effectively improves the accuracy of multi-label vulnerability classification.