Image captioning is crucial for multimodal understanding, bridging visual content and natural language. Despite recent advancements in Large Multimodal Models (LMMs), when faced with unseen entities or scenes in the open world, even when attempting to leverage learned knowledge, models still struggle with vague and inaccurate descriptions, and may even generate knowledge hallucinations. A key reason is that the model fails to effectively integrate knowledge with visual information, limiting its understanding of visual content. Thus, we propose Adaptive Knowledge Graph-guided Multimodal Alignment (AKGMA) for image captioning, which enhances semantic understanding in open-world scenes through visual knowledge reasoning, reducing knowledge hallucinations and improving caption quality. It consist three key components: Entity-guided Knowledge Aligner (EKA), Adaptive Knowledge Graph Construction (AKGC), and Scene-Context Knowledge Adapter (SCKA). EKA connects visual entities to knowledge graphs, providing structured knowledge to a small language model, which interacts with a visual encoder to acquire visual knowledge. AKGC uses reinforcement learning to build image-relevant subgraphs to optimize knowledge prompts and improve knowledge hallucinations. SCKA leverages scene graph annotations to extract visual contextual knowledge and inject it into Large Language Models (LLMs), ensuring the generated descriptions are consistent with the image's details. Additionally, we introduce UniKnowCap, a new image knowledge description dataset spanning various open-world knowledge domains, designed to evaluate the knowledge accuracy and detail consistency of model-generated descriptions. Extensive experiments show our model outperforms baselines across multiple metrics.