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This thesis planned to find drivers for young adults continued knowledge sharing in virtual academic communities and helped VACs managers find significant factors that affect young adults.We used Smart Partial Least Squares (PLS) 3.0 to analyze data.On the basis of the analysis results, hypotheses were judged and results were discussed.Finally, we gave suggestions to management of virtual academic communities.Through this research, we found that reputation, academic information quality, academic information quantity and user interface are four drivers for young adults confirmation of beliefs.And reputation and social relationship are two influence factors for young adults perceived usefulness.In the above factors, satisfaction is the most important factor affecting young adults continued knowledge sharing.At the end of thesis, four suggestions were given to Chinese VACs managers. This is a quantitative research based on a survey.There are six chapters in this study. The first chapter is an introduction to research theme.We discussed research background and illustrated the similarities and differences between the representative Chinese and global VACs.Combined with research interest and research background, we decided on the purposes and procedures of this study. Then, the second chapter is literature review.We combined continued knowledge sharing and VACs to analyze the prior researches.At the end of this chapter, expectation-confirmation model and IS success model were introduced. Based on theories and research status, we built the model in third chapter.The mainly body of model used ECM-IS model.IS success model as a basis to extend external variables, they are reputation, social relationship trust, academic information quality, academic information quantity and user interface. The forth chapter showed research methodology.According to this research model, we did a survey in the form of electronic questionnaire.Data was collected from young adults who have used Chinese VACs. Next chapter is data analysis.We used SmartPLS3.0 for data analysis, and then PLS Algorithm and Bootstrapping algorithm was used to test constructs reliability, validity, and significant. At the end of the thesis, in accordance with aforementioned explanations and analysis, we verified whether the hypotheses are correct or not and discussion and conclusions were illustrated.