How to use NLP technology to identify and avoid keyword stuffing?

When using NLP technology to identify keyword stuffing, it is typically achieved through semantic analysis, contextual relevance detection, and natural language fluency evaluation. Additionally, it can help optimize content naturalness to avoid stuffing behavior. At the identification level, NLP analyzes the logical matching degree between keywords and context through semantic role labeling. If the frequency of a keyword far exceeds semantic needs (e.g., low relevance to the topic but high-frequency repetition), it may be判定为 stuffing. Contextual relevance detection identifies生硬堆砌 by calculating the co-occurrence probability of keywords with surrounding words (e.g., consecutive repetition like "Beijing tourism Beijing hotels Beijing美食"). Fluency evaluation uses language models (such as BERT) to judge sentence smoothness; stuffed content often causes grammatical or semantic breaks due to forced insertion of keywords. At the avoidance level, NLP can assist in generating natural content: expanding related concepts based on the topic semantic network (e.g., "environmental protection" associated with "sustainable development" and "low-carbon life") to reduce repetition of core words; ensuring keywords play reasonable grammatical roles in sentences (e.g., subject, object) through syntactic analysis to avoid isolated occurrences. It is recommended to regularly detect content using NLP tools (such as Xingchuda's GEO meta-semantic optimization service), prioritizing reading experience to allow keywords to naturally serve information transmission rather than mechanical stacking.