Study on Data Mining Technology for Online and Offline E-Commerce Users Based on Big Data

Data mining technology can comprehensively analyze the feedback of these products. At the same time, it can also limit the interpretation of time and other data, making the results of its data analysis more accurate and reliable. After analyzing user feedback, adjust the product to meet user needs and promote corporate profitability. This article synthesizes the online e-commerce management data level indicators of multiple enterprises. Statistical analysis and integration of user mining models are conducted through analysis of data mining and enterprise model data. Conduct statistics on the data results through surveys. The development results of group data were evaluated through simulation operations. This questionnaire is distributed online in the form of a questionnaire star, which can effectively avoid the limitations of the questionnaire. In the corresponding tests, a total of four sets of data from 2000 to 2015 were selected to test the response of the entire algorithm under different user connections. In traditional mode, the response time has increased from 60 seconds to 500 seconds. Based on this algorithm, the response time is greatly shortened. When the algorithm carries an extreme value, the response time is about 700s. The test results show that the statistical accuracy of the optimal management model is generally 95%. Above, the accuracy rate of traditional algorithms is generally below 80%. The management model in this article has the shortest calculation time, averaging only 10 seconds. The research results show that the user electronic management model proposed in the study has a good labeling level. It can help enterprises conduct more reasonable and meaningful management.