This study aims to investigate the current situation and risk of innovation and entrepreneurship of science and technology workers. Questionnaire surveys were conducted with a random sample of 18,629 science and technology workers using the National Survey Site of the China Association for Science and Technology. Besides, field research was conducted on 9 technology-based startups and incubators and had interviews with science and technology workers, technology management experts, and entrepreneurial tutors. The study shows that the willingness for entrepreneurship is increasing. However, most science and technology workers stay in a wait-and-see stage and their entrepreneurial actions vary with the implementation of innovation policies. Some potential science and technology entrepreneurs dare not to leave their jobs as they have worries. It is necessary to further crack policy difficulties and institutional obstacles and improve the ecological environment conducive to innovation and entrepreneurship.

Although many studies have discussed the positive impacts of organizational incentive system and salary on scientific research performance, few have empirically examined the positive effect of work-related flow, or estimated the difference due to the level of flow among scientific researchers in China. A survey was sent to 772 scientific researchers from 20 universities in China, considering gender, age, professional rank, subject, region, institutional category and educational background as the 7 demographic variables. By conducting difference test and regression analysis, it is found that work-related flow is positively related to scientific research performance. Among the demographic variables, educational background, region and institutional category cause the largest differences of the level of work-related flow while professional rank and subject have no significant influence. This study explains not only why scientific researchers with the same background conditions perceive different level of individual work-related flow but also why scientific researchers with different background conditions perceive different levels of overall work-related flow. We also suggest that universities need to pay more attention to work-related status and cognitive factors of scientific researchers in the future.

Based on the grey system theory, this paper studies unbalanced distribution of regional scientific and technological talents in China and calculates the main factors affecting the unbalanced distribution. On this basis, a grey system prediction model of scientific and technological talents is established to simulate and predict the numbers of regional scientific and technological talents in China. The result shows that the regional difference Matthew effect is obvious, that the siphon effect continues to exist, and that the risk of barrel short board increases. Corresponding countermeasures and suggestions are put forward. The result of the study has certain reference value for controlling the risk of extremely unbalanced distribution of scientific and technological talents in China as well as coordinating regional development

Talents are the foundation of entrepreneurship and the driver of prosperity. The essence of innovation-driven development is talent-driven. Scientific talents are the most critical driving force and decisive factor in the development of economy. There are fierce talent competitions between different regions in China. In this paper we abstract talent migration between different provinces in China as a complex network, and propose an associated networked model. Based on bibliometrics, we get the data about talent migration between different provinces from 2010 to 2017. We combine the data and the propose a networked model to study the evolution of talent migration in China by analyzing the evolution of some indicators of the network. In addition, we also study the talent migration flows in a view of obstacle factors by the way of assuming an obstacle-free framework. We hope that the results can be used to make policies about talents for governments and support to analyze social problems in quantification.