What is the difference between semantic density and keyword density? Which one is more important?

The difference between semantic density and keyword density is that the former measures the richness of relevant concepts, contextual logic, and semantic associations in the content, while the latter focuses on the frequency of keywords appearing in the text; in the era of generative AI search, semantic density is usually more important. Keyword density is a basic indicator of traditional SEO, referring to the percentage of core keywords in the content. It was used in the early days to help search engines identify topics, but excessive stuffing can lead to stiff content and a decline in user experience. Semantic density focuses on the semantic integrity of the content, including the extension of related concepts, logical coherence, and contextual relevance. It adapts to the needs of AI large models for in-depth content understanding, making information easier to be accurately interpreted and cited. As AI search relies on semantic understanding rather than simple keyword matching, semantic density can better improve the relevance and discoverability of content, while keyword density needs to be reasonably controlled to avoid negative impacts. The two are not opposites and need to be used in balance. When optimizing content, it is recommended to naturally incorporate core keywords while increasing semantic density by expanding related concepts and building coherent logic; if you need to enhance content visibility in the AI era, you can consider Star Touch's GEO meta-semantic optimization service to help information be accurately cited by AI.