Research on the Development of Chinese Industrial Products e-Commerce Based on Text Mining

Policy and report texts related to industrial products e-commerce carry rich and important information, which are crucial for the government to formulate relevant policies and enterprises to plan development paths. In order to study and grasp the development status of China’s industrial products e-commerce, 18 industrial e-commerce policy texts and 10 industrial e-commerce reports are deeply mined and analyzed by using text mining methods. Firstly, natural language processing technology is used to pre-process the industrial products commerce-related policy and industry report text data. Then, word frequency statistics and TF-IDF keyword extraction are carried out, and the results of word frequency statistics are displayed visually. Then the feature set is obtained by combining the manual screening method, and the original text corpus is used as the training set using the skip-gram model in word2vec, and the feature words are transformed into word vectors in the multi-dimensional space. Finally, use the k-means clustering algorithm to group the features. The research results based on text mining provide supportive decisions for promoting the development of China’s industrial products e-commerce industry.