With the rapid development of the Internet and the rapid change of the era of big data, data mining has played a great role in exploring and discovering potential valuable information from the vast sea of data and has become one of the most popular research and practice fields. At the same time, the emerging marketing model of network marketing is sweeping the market, and network marketing is increasingly replacing the traditional marketing model with its unique advantages, such as wide coverage, fast dissemination, and being more flexible and targeted. Preserving, researching, and analyzing the behavior of network marketing users are the difficulties of current network marketing. It is the focus of this paper to explore how to use Web data mining technology to model the user behavior of Internet marketing. For the above problems, this paper used the weight calculation algorithm of users’ daily behavior feature items and the sequential pattern discovery algorithm based on multiple factor constraints to scientifically construct the online marketing user behavior model. The experimental results have shown that the sequential pattern discovery algorithm based on multiple factor constraints could save 30%-40% of the running cost when building a user behavior model. At the same time, the user behavior model constructed by this algorithm has improved the simulation and prediction accuracy of user behavior by 37%, which showed that the use of Web data mining to build network marketing user behavior model could provide an objective basis for the strategic development of network marketing.