Purpose/Significance. This paper aims to explore the influence mechanism of adolescent health information literacy on health behavior. Method/Process. 13–19 year olds are taken as the survey objects to investigate their health information literacy through a questionnaire. Health information literacy mainly includes health information needs, acquisition, evaluation, use, and behaviors. A total of 252 adolescents’ data were collected in this study, and model testing was performed with the help of regression analysis and structural equation modeling. Conclusion/Results. The results of the study show that adolescents’ health information needs, acquisition, evaluation, and application abilities have a positive impact on health behaviors in the social network environment. Emotional responses and individual cognition as intermediate variables play important roles between health information literacy and health behaviors. Health information needs and health information assessments have the highest impact on mental health and social health, respectively. The society should pay special attention to the influence of adolescents’ health cognition and anxiety on health behavior in the context of social network.
The effective means to stimulate economic growth is to enhance consumers’ consumption capacity. Because many consumers have different consumption habits, they will pay different attention to products. Even the same consumer will have different shopping experiences when buying the same product at different times. By mining the online comments of consumers on the online fitness platform, we can find the characteristics of fitness projects that consumers care about. Analyzing consumers’ emotional tendencies towards the characteristics of fitness programs will help online fitness platforms adjust the quality and service direction of fitness programs in a timely manner. At the same time, it can also provide purchase advice and suggestions for other consumers. Based on this goal, this study uses an optimized support vector regression (SVR) model to build a consumer sentiment analysis system, so as to predict the consumer’s willingness to pay. The optimized SVR model uses the region convolution neural network (RCNN) to extract features from the dataset, and uses feature data to train the SVR model. The experimental results show that the SVR model optimized by RCNN is more accurate. The improvement of the accuracy of consumer sentiment analysis can accurately help businesses promote and publicize, and increase sales. On the other hand, the increase in the accuracy of emotion analysis can also help users quickly locate their favorite fitness projects, saving browsing time. To sum up, the emotional analysis system for consumers in this paper has good practical value.
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