The age of big data has generated vast collections of texts, images, and digital traces for scientific analysis. Among the subfields of the social sciences, cultural research, particularly cultural sociology, has benefited most from these developments. Cultural big data and machine learning methods provide new research materials, analytical techniques, and theoretical perspectives. Drawing on large-scale corpora and online records, scholars can measure the spatial and temporal dimensions of culture and interpret patterns in textual and visual materials. Unsupervised machine learning methods such as the LDA topic model, Word2Vec algorithm, named entity recognition (NER), and sentiment analysis make it possible to identify themes, associations, and emotional tendencies within cultural data. Through these methods, researchers in the social sciences can better understand how cultural forms evolve and interact with broader social structures, providing new insights into the disciplinary trend of the “cultural turn.”