This article investigates an emerging social commerce model that integrates social networks and e-commerce, highlighting the pivotal role of topic marketing in influencing consumers’ purchasing decisions. Within the framework of user-generated content (UGC), and accounting for the complex interplay among factors influencing topic propagation as well as the cooperative and competitive dynamics between topics, this study proposes a marketing topic propagation model based on factor analysis and the Stackelberg game. First, factor analysis was employed to extract two primary factors influencing topic propagation—user behavior and external environmental factors—thereby reducing the dimensionality of the variables. A multiple linear regression model was subsequently applied to quantify the contribution of these factors, establishing a robust quantitative foundation for optimizing marketing strategies. Second, to account for the competitive and cooperative interactions among topics, the behavior of ordinary users and influencers was analyzed using a Stackelberg game framework. This analysis reveals the complex interdependencies between cooperative and competitive dynamics in topic propagation. Finally, to address the behavioral variability and time-varying nature of users’ active participation or neglect (of topics), the traditional susceptible-infectious-recovered (SIR) model was refined by subdividing the infectious state into “marketing” and “neglected” states, resulting in the susceptible-marketing-neglected-recovered (SMNR) dynamic propagation model. By incorporating a temporal dimension, the model captures dynamic changes in states and parameters during topic propagation, thereby enhancing the accuracy of predictions regarding propagation paths and trends. Experimental results demonstrate that the proposed model effectively measures dynamic changes in influencing factors, incorporates game-theoretic relationships, and accurately predicts the temporal evolution of marketing topics. Its dynamic characteristics provide a robust theoretical foundation for optimizing social commerce marketing strategies and offer precise guidance for data-driven marketing decisions.

In the context of social commerce, user dissemination behavior is influenced by the coupling of multiple factors, such as the timeliness of marketing content and the dynamics of social relationships. Existing methods are limited in predictive accuracy due to neglecting the cold-start data sparsity and the evolution patterns of behavior sequences. To address this issue, we propose a predictive model for user dissemination behavior in social commerce, leveraging transfer learning and sparse representation techniques. Firstly, to tackle the problem of sparse cold-start topic data, a cross-domain transfer learning framework is designed. Historical topics are matched as the source domain based on semantic similarity, and knowledge transfer is achieved through a domain adaptation loss function, thereby improving the model's performance for cold-start or low-engagement topics. Second, to address the challenge of high-dimensional feature modeling, a dual sparse coding method is proposed. This method performs low-rank decomposition on both the user attribute matrix and the social adjacency matrix, preserving core structural information while reducing computational complexity. Finally, to handle the dynamic propagation characteristics, a utility function combining tripartite game theory is constructed, incorporating the marketing content attractiveness, user social influence, and time decay factor. This is embedded into the weight generation module of the relationship-aware graph attention network. By using a dynamic time encoder, the evolution pattern of behaviors is captured, enabling accurate prediction of social commerce user behavior. Experimental results show that this model can not only accurately predict users' interactions with marketing topics but also reveal the dissemination patterns of marketing content, providing strong support for optimizing marketing strategies. This research offers an effective technical means for the precise placement of marketing content and user behavior analysis in social commerce.