Evaluation of College Students' Innovation and Entrepreneurial Ability for the Science and Technology Service Industry

Abstract

The development level of the science and technology service industry is an important factor affecting the development speed of regional economy and the formation of innovation ability and development potential of the region, and the construction of talent team is the core and foundation for the development of the science and technology service industry. To measure such ability, this paper constructed an evaluation model for the innovation and entrepreneurial ability (IEA) of college students. First, a corresponding evaluation index system was established, the quantifiable index data were subject to factor analysis, and the structural equation model was subject to regression estimation using the maximum likelihood method. Then, from multiple aspects such as the level of the colleges, the major of the students, and the gender of the students, this paper comprehensively analyzed college students’ IEA. And finally, based on one-way analysis of variance, the differences between indexes were analyzed, and a path analysis model was established to analyze the relationship between the science and technology service industry’s regional industrial scale, resource input, informatization level, spatial agglomeration degree, and college students’ IEA.

Research on the Cultivation of Interdisciplinary Innovation Ability of Students in Higher Vocational Colleges and Universities

“…Even so, the so-called high and low levels of innovativeness are relative and vary according to the stage of development and the target audience. Depending on the type of discipline and the level of development of innovative ability, it is possible to categorize the current development of students' innovative ability in the field of education into four types [3][4][5]. They are: "single-discipline-low innovation," "single-discipline-high innovation," "multidiscipline-low innovation, "and multidisciplinary-high innovation."…”

A Performance Prediction Method for Talent Team Building Based on Integrated ISA-BP Neural Networks

“…Currently, talent team construction performance prediction methods include fuzzy comprehensive evaluation method [9], machine learning method [10], neural network [11], integrated learning technology [12], deep learning method [13] and so on. Literature [14] deals with the performance evaluation weights of public security intelligence personnel through fuzzy theory, and proposes diversified incentive means and methods so as to improve the efficiency of human resources; Literature [15] utilizes hierarchical analysis method to solve the index weights based on the allround index system of university professors' scientific research work; Literature [16] uses more than two machine learning methods based on the performance data of the construction of the talent team through hierarchical analysis method weight optimization fusion to construct talent team construction performance prediction model, training test results show that the prediction method of multivariate isomorphism is conducive to the improvement of prediction accuracy; Literature [17] optimizes and improves the support vector machine through the use of swarm intelligent algorithm to construct university performance prediction method, so as to improve prediction accuracy and real-time; Literature [18] analyzes the results of the performance evaluation of the feedback is not timely, the performance evaluation of individual teachers and Teacher team performance assessment is inconsistent, teacher performance is not coordinated, performance and personal development can not be integrated, and other existing problems, the neural network as a prediction model, proposed a neural network-based talent team performance prediction method; Literature [19] combines the integrated learning and weak machine learning algorithms, proposed a talent team building performance prediction method based on integrated-support vector machine; Literature [20] through the construction of the talent construction performance system, establish a prediction model based on intelligent optimization algorithm to improve the deep learning method, which provides new ideas for the human resource prediction model. In response to the above literature analysis, the existing human team building performance prediction methods have the following defects [21]:…”

Research on Digital Management of Scientific and Technological Innovation Services in Universities Based on Artificial Intelligence Background

“…Literature [12] established an index system with R&D investment, technological innovation, and economic growth as the research variables and carried out an empirical analysis, pointing out that all the above three factors have stagnated and slowly declined, and it is necessary to continue to improve the incentive mechanism for scientific and technological innovation, improve the efficiency of capital investment and research and development, and at the same time, accelerate the marketing of the results of innovation. Literature [13] builds an assessment model of the innovation and entrepreneurship ability of college students to quantify the innovation and entrepreneurship ability of students and then optimize the construction of the talent team to provide innovative and applied talents for society. Literature [14] conducted research and analysis on the impact of academic incubators on the innovation quality of research-intensive academic institutions in the United States, pointing out that the application of academic incubators leads to a decline in the quality of innovation in colleges and universities, and at the same time, the incubator will grab resources with other campus programs, and there is internal conflict.…”