Modeling and Research on the Relationship between Japanese Monster Culture and Folk Beliefs in a Big Data Environment

This paper aims to explore the intrinsic relationship between Japanese yokai culture and folk beliefs in order to gain a deeper understanding of cultural contexts and the foundations of belief systems. First, the collected data was preprocessed using the Jieba word segmentation tool and stopword removal methods. Textual data was converted into computable structured data using a vector space model. The TF-IDF algorithm was employed to extract text features and perform dimensionality reduction, resulting in features with simple and clear vector dimensions. Next, a multidimensional network of Japanese yokai culture and folk beliefs is constructed, and social network analysis is applied to investigate the strength of feature associations between Japanese yokai culture and folk beliefs. Association rule algorithms are then introduced to further explore the association patterns between Japanese yokai culture and folk beliefs, and the obtained association relationships are visualized. The study shows that the average shortest path in the relationship network between Japanese yokai culture and folk beliefs is only 1.05. This network is influenced by multiple factors such as yokai types, regional distribution characteristics, and historical periods, indicating that the network has high interaction efficiency, enabling the rapid dissemination of Japanese yokai culture. It is not easily disrupted by external factors, making the relationship network between Japanese yokai culture and folk beliefs more adaptable over time and conducive to cultural inheritance.