The rapid development of blockchain token incentives provides a new perspective to solve the problem of insufficient motivation for user content creation, but it still faces many challenges in the actual implementation process. This paper takes Steemit, a knowledge community based on blockchain, as the research object.By collecting block data, this paper analyzes the situations and problems existed in the token incentive mechanism of community from two aspects, including incentive equality and knowledge contribution efficiency, thus revealing the problem of token incentive allocation monopoly. Moreover, this paper identifies the influence of token incentive allocation monopoly on user knowledge contribution. The results show that the token incentive distribution in the community is monopolized by a small number of top users, and the incentive distribution mechanism in the community cannot effectively reflect the users' knowledge contribution levels. The inequality of token incentives allocation results in the decrease of users' content production and content discovery levels.

Online health community has become an important platform for patients and their families to obtain health knowledge, the information service quality of the community can be enhanced by exploring the knowledge sharing behaviors of different types of users. Article based on the theory of knowledge collaborative construction, the user interaction network and knowledge network are constructed, based on the algorithms of the identification of key users and the discovery of key paths, the rules of knowledge sharing behavior are mined. This paper takes the community of biliary cancer as an example to carry out an empirical study, the results show that the knowledge interaction behavior model across multiple communities is formed under the guidance of key users, and ordinary users tend to interact with other users with the same disease types in the community. The knowledge sharing of key users focuses on preoperative examination and disease symptoms knowledge types, while the knowledge sharing of ordinary users focuses on preoperative examination knowledge types; The key knowledge paths reflect the knowledge needs of users in different stages of disease treatment.

Community detection is an important issue in the field of complex network research. However, the traditional methods that achieve community detection through the density of edges can not include the non-structural attributes of nodes. In this paper, we propose a method of community detection based on variational graph auto-encoders (VGAE) incorporating node attributes, namely VGAE-INA, and then test the method by real network data in two different fields. Through the experiment, we find that the modularity obtained by the proposed method is not significantly different from the traditional methods (such as the Louvain method) and the methods based on deep learning (such as the node2vec method), but the node similarity in the community is much higher than these methods. This result indicates that through unsupervised iterative learning, VGAE-INA can effectively detect the network community under the condition of considering both connections and attributes of nodes. At the same time, our method also lays the groundwork for the performance improvement of practical applications based on community detection such as personalized recommendations and opinion mining in crowds.

This paper introduces LDA thematic modeling analysis method for the first time, from the perspective of policy tools, in-depth content mining of the policy text of "Opinions on Comprehensively Deepening the Reform of Teacher Team Construction in the New Era" in 27 provinces and municipalities directly under the Central Government. Firstly, three major themes of policy texts are mined through LDA and the use of policy tools is analyzed. Secondly, the regional differences of the excavated policy themes are analyzed. Finally, it puts forward "pay attention to and take into account the dynamic needs of different subjects, coordinate policy demands"; Establish a structured system of policy objectives and choose policy tools rationally; Break the dependence of source path in local policies, and choose hot topics and tools according to local conditions. Text content through policy on the innovative use of quantitative research methods and policy tools perspective, makes the interpretation of this study has certain theory and practice guidance, to provide more scientific and objective evidence for policy making, especially for local explanatory policy making, and realize the dual unity of subjective and objective, experience and interpretation of teacher education policy research.

Blockchain technology has shown good promise for overcoming the limitations of traditional digital content platforms. However, the currently available blockchain-based digital content platforms still have certain shortcomings. It is necessary to further study the technical architecture and business operation model of such platforms. In this regard, this paper designs a framework for a digital content platform based on federated blockchain, which incorporates multiple participants of digital content operation into the platform system and provides an integrated solution for digital content operation and IPR governance; at the same time, the consortium blockchain forms a network with joint participation and management of multiple organizations, forming an endogenous competition mechanism and inhibiting monopoly of platform operators. In line with the designed platform architecture, the consensus reaching and certified institutional reputation adjustment mechanism of the platform is further designed based on the PoA consensus mechanism, coupling the consensus process with the institutional reputation adjustment process to promote institutions to better provide platform services. Based on the proposed platform architecture, the corresponding business operation mode is further discussed.