The purpose is to further promote the integration of advanced technology in the field of drama. Under the background of artificial intelligence of Internet of things, drama language is analyzed by using a clustering algorithm of data mining technology. The original K-means algorithm is compared with the improved K-means algorithm through the use and analysis of the corresponding data mining methods. Latent Dirichlet Allocation (LDA) topic model clustering is used to test the randomly selected texts. The results suggest that the improved algorithm is more reliable and efficient in data analysis and clustering effect, which proves the availability of the improved K-means algorithm. The specific research content can provide a scientific and effective reference for the follow-up research of text mining analysis.

目的是进一步推动先进技术在戏剧领域的融合。在物联网人工智能背景下，利用数据挖掘技术的聚类算法对戏剧语言进行分析。通过对相应数据挖掘方法的使用和分析，将原始K-means算法与改进的K-means算法进行比较。潜在狄利克雷分配 (LDA) 主题模型聚类用于测试随机选择的文本。结果表明改进算法在数据分析和聚类效果上更加可靠和高效，证明了改进K-means算法的有效性。具体研究内容可为文本挖掘分析的后续研究提供科学有效的参考。