E-commerce with the immersive scene of live streaming gives users a new interaction and consumption experience, extending a new path for traditional marketing. Live barrage, being the primary means of users’ social engagement in live e-commerce, encapsulates their inclination to purchase products, commonly referred to as consumption intention. The mining of live barrage enables the exploration of latent user behavior and consumption intention. The current methods employed for mining consumption intention in e-commerce live barrage encounter challenges including insufficient corpus, excessively brief text, and significant semantic deficiencies. Therefore, this study proposes a paradigm of consumption intention recognition by combining the text features of barrage: by constructing a dataset of Live barrage, cleaning the original dataset, and then using a fused Bert’s feature network model Bert-BiLSTM to identify the consumption intention in live e-commerce Live barrage. The experimental results demonstrate the efficacy of the proposed method for recognizing consumption intention. Finally, this study conducted text mining on live barrage containing consumption intent, including word cloud visualization, topic extraction, and co-occurrence analysis, further investigating consumers’ purchasing needs and intentions.

Facial expressions, whether simple or complex, convey pheromones that can affect others. Plentiful sensory input delivered by marketing anchors' facial expressions to audiences can stimulate consumers' identification and influence decision-making, especially in live streaming media marketing. This paper proposes an efficient feature extraction network based on the YOLOv5 model for detecting anchors' facial expressions. First, a two-step cascade classifier and recycler is established to filter invalid video frames to generate a facial expression dataset of anchors. Second, GhostNet and coordinate attention are fused in YOLOv5 to eliminate latency and improve accuracy. YOLOv5 modified with the proposed efficient feature extraction structure outperforms the original YOLOv5 on our self-built dataset in both speed and accuracy.