A Classification Method for National Culture Propagation Using Deep Learning

Abstract

This paper combines the artificial intelligence (AI) and deep leaning technologies to classify the spread of national culture. First, we use the Python language to build a crawler technology in order to obtain the sample data of national culture from authoritative websites. Then, we use the natural language processing (NLP) expertise to analyze and preprocess the text of national diversity. In this way, we realize vectorization and the feature word extraction of the text related to national cultural resources based on the doc2vec technology. The vectorized texts of national cultural resources are clustered based on the K-means clustering technique. Moreover, the elbow rule process is implemented to determine the optimal number of clustering clusters. Finally, the text association relationship of the national cultural resources is obtained. Moreover, this paper adopts the unsupervised training method, which can reveal the semantic information contained in the text of national cultural resources. This can also help us during the identification process of the category connection amongst the texts of national cultural resources and provide us with methodological support for the gathering, storing, and other smart services of enormous national cultural resources. The outcomes demonstrate that the correctness rate of the suggested algorithm is greater than the accuracy of the linear regression and can reach up to 80%.