Application of user preference mining algorithms based on data mining and social behavior in brand building

Small and medium-sized enterprises currently suffer from a lack of branding. Therefore, to further promote their active branding, this study proposes a user preference mining algorithm based on data mining and social behavior. Employing this algorithm to study the degree of users’ brand preference can provide data support for enterprises’ brand building. The experimental results showed that the proposed algorithm outperforms previous algorithms in terms of performance, convergence, and accuracy. The area under the curve reached 0.953, indicating highly authentic output results with extremely high realism. In actual simulation experiments, its prediction results for the user’s brand preference index are accurate, with an error of only 0.11, and the algorithm has extremely high ratings among industry insiders. In conclusion, the user-preference mining algorithm based on data mining and social behaviors suggested in this study plays a better role in promoting an enterprise’s brand building. It can help the enterprise know the level of consumer preference for its brand; accordingly, it can determine the shortcomings in, provide effective and accurate data support for, and thereby promote its brand building.