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This paper conducts a preliminary exploration of Artificial Intelligence (AI) for emotion recognition, particularly in its business applications. Employing adaptive technologies like machine learning algorithms and computer vision, AI systems analyze human emotions through facial expressions, speech patterns, and physiological signals. Ethical considerations and responsible deployment of these technologies are emphasized through an intense literature review. The study employs a comprehensive bibliometric analysis, utilizing tools such as VOSViewer, to trace the evolution of emotion-aware AI in business. Three key steps involve surveying the literature on emotion analysis, summarizing information on emotion in various contexts, and categorizing methods based on their areas of expertise. Comparative studies on emotion datasets reveal advancements in model fusion methods, exceeding human accuracy and enhancing applications in customer service and market research. The bibliometric analysis sheds light on a shift towards sophisticated, multimodal approaches in emotion recognition research, addressing challenges such as imbalanced datasets and interpretability issues. Visualizations depict keyword distributions in research papers, emphasizing the significance of “emotion recognition” and “deep learning.” The study concludes by offering insights gained from network visualization, showcasing core keywords and their density in research papers. Based on the literature, a SWOT analysis is also conducted to identify the strengths, weaknesses, opportunities, and threats associated with applying emotion recognition to business. Strengths include the technology’s high accuracy and real-time analysis capabilities, enabling diverse applications such as customer service and product quality improvement. However, weaknesses include data bias affecting the AI model’s quality and challenges in processing complex emotional expressions. Opportunities lie in the increasing number of studies, market size, and improving research outcomes, while threats include privacy concerns and growing competition.

In recent years, the traditional folk culture as the core of the tourism, culture industry and derivative industry has driven the development of regional economies in Jiaodong. How to develop and utilize the traditional folk culture resources in the region, make them into folk culture tourism products and bring economic benefits have become a new research topic in related fields. This paper takes Jiaodong traditional folk culture as a starting point, folk culture tourism products as the research object, from the practical, creative, cultural, historical and service design point of view, analysing Jiaodong traditional folk culture in folk culture and tourism products in the current situation and its defects, and provides ideas for the sustainable development of Jiaodong traditional folk culture in folk culture and tourism products.

Based on community medical APP as the carrier, this paper studies the emotional and experiential process of community doctors using mobile community medical APP under different cognitive loads, and puts forward the improvement of product optimization. Firstly, based on the cognitive load theory, objective user data is obtained through cognitive load control experiments on target users. Second, ask the target user to fill in the Positive and Negative Affect Scale and System Usability Scale to obtain subjective user data. Finally, analyze the user’s subjective and objective data. Based on the analysis of experimental results and user observation, the improvement strategy is proposed to reduce the barriers to community doctors learning and using community medical apps.