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In recent years, database technology has developed rapidly and is changing with each passing day. With the support of network technology, its application scale, scope, and depth are constantly expanding. With the explosive growth of data, we are also faced with such a challenge: First, as a basic information storage and management method, database technology can only perform simple data processing, such as query, statistics, reports, etc.; lack of decision-making; analysis; prediction; and other advanced functions. Secondly, in the face of these massive data, people pay more attention to how to dig out the important information hidden in these data, rather than the data itself. Therefore, data mining technology, which integrates statistics, artificial intelligence, pattern recognition, and optimization, emerges as the times require. Data mining technology is application-oriented from the very beginning, and its great success in various industries has fully demonstrated its strong vitality, especially in the retail industry. If the data mining technology can be perfectly combined with the retail industry, it can not only bring great convenience to customers but also inject new vitality into enterprises, making them invincible in the fierce competition. This paper proposes a filtering of high-quality customer system framework based on maximum entropy, which expresses customer data as a feature vector for feature selection and feature smoothing. The filtering performance of different feature sets is compared by combining different characteristics of customer data. Experiment and conduct multimedia presentations. Experiments show that the filtration performance of this system is better than the general filtration system.

Research on the Application of Multimedia Entropy Method in Data Mining of Retail Business