This paper discusses the use of big data analytics to understand consumers’ buying behavior using the enhanced K-means clustering method/algorithm. This study aims to develop coherent and precise marketing strategies for consumers’ purchase behavior that are better than existing learning models. It creates a user-value model by learning and modeling the information of a particular online enterprise. A model for various categories of consumers is built using the enhanced K-means algorithm, in this, the purchase behavior of the users is divided into clusters, then a customer value matrix is constructed and the links between data points based on clustering are explored. This gives the business managers a reference point from a sales point of view. The e-commerce users are classified into three categories by marking points. It has also been observed that if the sample size increases continuously, feature points and multidimensional matrices can be formed with the sample resulting in better results. This algorithm is also compared to other existing algorithms including DBSCAN, mean shift clustering, and hierarchical clustering. The final results imply that the enhanced K-means algorithm is stable and efficient. It also shows that the analysis of user clustering characteristics helps build more explicit and efficient trade plans.