Exploring the Features and Trends of Industrial Product E-Commerce in China Using Text-Mining Approaches

Industrial product e-commerce refers to the specific application of the e-commerce concept in industrial product transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industrial literature, such as policies, reports, and standards related to industrial product e-commerce, contains much crucial information. Through a systematical analysis of this information, we can explore and comprehend the development characteristics and trends of industrial product e-commerce. To this end, 18 policy documents, 10 industrial reports, and five standards are analyzed by employing text-mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial product commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the word frequency statistics are visually represented. Subsequently, the feature set is obtained by combining these processes with the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in Word2Vec, and the feature words are transformed into word vectors in the multi-dimensional space. The K-means algorithm is used to cluster the feature words into groups. The latent Dirichlet allocation (LDA) method is then utilized to further group and discover the features. The text-mining results provide evidence for the development characteristics and trends of industrial product e-commerce in China.