Abstract: To address data bias in medical imaging and reporting at both the data content and distribution, a medical report generation method based on multi-source knowledge cross-modal alignment and fusion (MRGM-MKCAF) is proposed. First, radiology-related medical concepts were used as prior knowledge, and case images and reports were used as empirical knowledge to construct a multi-modal knowledge base. Subsequently, a cross-modal alignment method employing multi-head attention mechanism was proposed, which used multiple attention heads to focus on the subspaces of various modal features in the knowledge base and performed fine-grained cross-modal feature alignment to obtain more comprehensive and accurate encoded features. Finally, a decoding module based on gating mechanism was proposed to dynamically select features from multi-source encoded features, removing redundancy and irrelevant information, which was then used for report generation. Experimental results on two datasets show that the proposed method significantly enhances the accuracy, fluency, and completeness of generated reports.