Knowledge graph (KG), as structured representations of knowledge, has a wide range of applications in the medical field. Entity alignment, which involves identifying equivalent entities across different KGs, is a fundamental step in constructing large-scale KGs. Although extensive research has focused on this issue, most of it has concentrated on aligning pairs of KGs, typically by capturing the semantic and structural information of entities to generate embeddings, followed by calculating embedding similarity to identify equivalent entities. This study identifies the problem of alignment error propagation when aligning multiple KGs. Given the high accuracy requirements for entity alignment in medical contexts, we propose a multi-source Chinese medical knowledge graph entity alignment method (MSOI-Align) that integrates entity semantics and ontology information. Our method pairs multiple KGs and uses representation learning to generate entity embeddings. It also incorporates both the similarity of entity names and ontology consistency constraints, leveraging a large language model to filter a set of candidate entities. Subsequently, based on triadic closure theory and the large language model, MSOI-Align automatically identifies and corrects the propagation of alignment errors for the candidate entities. Experimental results on four Chinese medical knowledge graphs show that MSOI-Align significantly enhances the precision of the entity alignment task, with the Hits@1 metric increasing from 0.42 to 0.92 compared to the state-of-the-art baseline. The fused knowledge graph, CMKG, contains 13 types of ontologies, 190000 entities, and approximately 700000 triplets. Due to copyright restrictions on one of the KGs, we are releasing the fusion of the other three KGs, named OpenCMKG.