<p>Accurate recognition of components in heating, ventilation, and air conditioning (HVAC) drawings is crucial for building information digitization. Existing recognition methods lack the ability to integrate multi-source information, such as component symbols, textual annotations, connection relationships, and domain knowledge, into a unified reasoning process. As a result, they exhibit poor performance when applied to HVAC drawings with diverse and non-standardized component symbols. To address these challenges, this paper proposes a confidence-driven method integrating large language models (LLMs) with computer vision (CV). A structured representation approach is designed to preserve spatial and semantic relationships in HVAC drawings, enabling the LLM to reason components beyond raw image inputs. A confidence-weighted mechanism assigns confidence scores to multi-source information, allowing high-confidence evidence to iteratively guide the refinement of uncertain component classifications. A dynamic knowledge infusion strategy is introduced to integrate HVAC-specific knowledge into the reasoning loop, allowing the reasoning process to produce domain-consistent component recognition. The proposed method is compared to conventional CV models on open-access HVAC drawing datasets. Results show that the true positive rate for every component category exceeds 0.89. Significant improvements of 46.4% in recall, 14.5% in precision, and 36.0% in F1-score over the baseline. Additionally, the model achieves a macro-average area under the curve (AUC) of 0.977, verifying its robust discrimination capability. The proposed framework effectively enhances the accuracy and robustness of HVAC component recognition, providing a scalable solution for automated building digitization.</p>