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Knowledge sharing among teachers is one of the important ways to improve their teaching and research ability. From the perspectives of teachers' knowledge sharing willingness, knowledge sharing ability, knowledge sharing environment, knowledge sharing technology support, knowledge sharing effect, and so forth, this paper constructs a teacher knowledge sharing behavior evaluation model, develops a knowledge sharing behavior evaluation index system, and proposes a comprehensive teacher's knowledge sharing behavior evaluation method based on the improved entropy-TOPSIS method. This is a comprehensive evaluation method combining subjective and objective weights, which avoids the subjectivity of traditional expert evaluation methods and other multilevel and multi-index weight determination methods and makes the evaluation results more objective, accurate, and more realistic. Finally, by taking the example of evaluating knowledge sharing behavior of teachers in a university of Chongqing, China, this paper verifies the feasibility and practicability of the proposed comprehensive teachers' knowledge sharing behavior evaluation system and method. [ABSTRACT FROM AUTHOR]

In the past, the extreme value standardization of indicators, the traditional weighting method, and the multifactor comprehensive model of land intensive use inevitably linearly correlate the evaluation indicators with the evaluation objects, ignoring the direction differences of different indicators in different intervals. At the same time, these methods are also difficult to meet the change of evaluation index weight value with land use type, and cannot adapt to the actual situation of land use environment level and dynamic change. Considering the objectivity of nonlinear correlation moderate index and weight assignment, based on the standardization of quadratic function index and entropy assignment, this paper studies the intensive and dynamic use of land in development zones by different regions to improve the realistic fit of the evaluation model. The results show that the overall level of land intensive use in Chongqing center district and western Chongqing is better than that in northeast Chongqing and southeast Chongqing, roughly showing the state of "high in west and low in east." [ABSTRACT FROM AUTHOR]

Higher education plays an important role in the improvement of people's quality and the development of our country. Therefore, it is necessary to evaluate the higher education curriculum. This paper analyzes and constructs the deep network learning system and self-encoder and evaluates the Chongqing higher education curriculum based on the deep learning network selected by 50 universities in Chongqing. It is found that the numbers of test objects, indicators, and hidden layers have an impact on the evaluation results. At the same time, a classroom teaching model is designed to improve the quality of higher education and solve the problem of insufficient curriculum quality of higher education. [ABSTRACT FROM AUTHOR]