To address the problems of insufficient global semantic representation and weak local discriminability in current short-text clustering methods， a Dual-Channel Feature Fusion representation method for short-text clustering based on Large Language Model （LLM）， named DCFF， was proposed. From a global perspective， a semantic-enhanced pseudo-label contrastive learning module was established， in which the LLM-generated keyword phrases were dynamically weighted and fused with original texts to enrich representations. Furthermore， high-confidence pseudo-labels were produced via self-adaptive optimal transport， while intra-cluster compactness and inter-cluster separation constraints were integrated into end-to-end training to achieve globally consistent embeddings. From a local perspective， a triplet representation optimization module based on entropy and discrepancy was established， which filtered high-informativeness samples via entropy and discrepancy. The embedding model was then fine-tuned with a confidence-weighted loss and a denoising mechanism to generate a vector representation with strong local discrimination. Finally， the global and local representations were fused using self-attention mechanism for direct application in clustering algorithms. Comparative experimental results on eight public short text clustering datasets against mainstream baselines showed that DCFF outperformed the baselines in accuracy on all datasets， achieving the lowest improvement of 3.19 percentage points on the GoogleNews-T dataset； in Normalized Mutual Information （NMI）， DCFF outperformed the baselines on six datasets， achieving the lowest improvement of 3.46 percentage points on the SearchSnippets dataset. The experimental results demonstrate that DCFF is well-suited for clustering tasks in various scenarios.
