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By means of practical case studies and site visits, this paper uses the Academic Week held in School of Architecture and Fine Art of Dalian University of Technology as the research objects and explores the organizational pattern of student-arranged activities in universities. The results demonstrate that during the activity, diversified contents and forms as well as academic platforms composed of multiple subjects can stimulate students' academic competence, thereby improving the academic environment. Moreover, the paper has also revealed the problems in the activity and provided relevant suggestions in this regard. This aside, it provides a new method to explore research-oriented instruction and it also enhances the overall academic ambience, and the arrangement of this event has greatly promoted the research and academic level of the school, effectively improving its reputation in the field and sector.

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With the rapid development of information technology, the development of information management system leads to the generation of heterogeneous data. The process of data fusion will inevitably lead to such problems as missing data, data conflict, data inconsistency and so on. We provide a new perspective that combines the theory in geology to conclude such kind of data errors as structural data faultage. Structural data faultages after data integration often lead to inconsistent data resources and inaccurate data information. In order to solve such problems, this article starts from the attributes of data. We come up with a new solution to process structural data faultages based on attribute similarity. We use the relation of similarity to define three new operations: Attribute cementation, Attribute addition, and Isomorphous homonuclear. Isomorphous homonuclear uses digraph to combine attributes. These three operations are mainly used to handle multiple data errors caused by data faultages, so that the redundancy of data can be reduced, and the consistency of data after integration can be ensured. Finally, it can eliminate the structural data faultage in data fusion. The experiment uses the data of doctoral dissertation in Shanghai University. Three types of dissertation data tables are fused. In addition, the structural data faultages after fusion are processed by the new method proposed by us. Through the statistical analysis of the experiment results and compare with the existing algorithm, we verify the validity and accuracy of this method to process structural data faultages.