[Purpose/Significance] AI technology, exemplified by AI-generated content, facilitates the emergence of new quality productive force and multi-dimensional value implications, which can better assist China's digital economy to achieve high-quality development and better promote the innovation of knowledge and intelligence services in China. Analyzing the new mechanisms, risks, and paths of AIGC-enabled knowledge and intelligence innovation services holds significant theoretical and practical importance for promoting the vigorous development of the discipline of information resource management under the major national strategy of new productive force. [Method/Process] Based on the theoretical connotation of new quality productive force, the paper analyzed the internal mechanism of AIGC-enabled knowledge and intelligence service innovation from the perspectives of labor objects, laborers, and labor materials. It identified the risks of AIGC-enabled knowledge and intelligence service innovation from the three dimensions of information source, channel, and information receptor, based on the three elements of information transmission, and proposed governance strategies. The paper also explained the practical path of AIGC-enabled knowledge and intelligence innovation services from the perspective of the composition of new productive force. [Result/Conclusion] The paper shows that under the new quality productive force strategy, AIGC forms an internal mechanism for knowledge and intelligence service innovation. This mechanism relies on data elements, uses basic models for support, and features human intelligence collaboration as its core. The paper also indicates a need for better management of data privacy and security risks, cyberspace security risks, and scientific and technological ethics security risks. This requires leveraging the collaborative advantages of industry, academia, and research. This paper proposes a practical path for promoting AIGC-enabled knowledge and intelligence service innovation. This path involves constructing a new model driven by data elements, forming a new team centered on human-intelligence collaboration, and developing reliable and trustworthy intelligent models.

[Purpose/Significance] New quality productive force is an important driving force for technological progress and economic development. [Method/Process] In order to promote the development of new quality productive force and promote the journey of Chinese modernization, this paper analyzed the mechanism of scientific data element trading to help realize new quality productive force, probed into the current practical difficulties faced by scientific data element trading, and gave the way to break the situation. [Result/Conclusion] The research shows that scientific data element refers to a new quality production element in which scientific data and its derivative data products participate in scientific and technological production and management activities to meet the needs of scientific and technological development and bring technological breakthroughs and economic benefits to owners or users, and is one of the core elements of new quality productive force. However, in the process of transaction, there are some problems, such as the friction of the interests of transaction subjects, the crisis of data black market, the weak policy of scientific data transaction, and the insufficient supply of scientific data resources. It is suggested to set up a scientific data bank, build a three-level connected data market, issue a special data transaction policy, and build a big data alliance chain to solve the current dilemma.

[Purpose/Significance] Propose relevant strategies for promoting the development of new quality productive force through data-driven think tank construction. [Method/Process] Starting with a review of the relationship between "data-driven think tank construction" and the development of "new quality productive force", deeply analyzed and refined the main characteristics and development directions of the new think tank construction paradigm represented by constructing data-driven think tanks in the context of new quality productive force development. [Result/Conclusion] To meet the requirements of developing new quality productivity, it is essential to promote the transformation of think tanks towards data-driven approaches. Specific strategies include prioritizing the transformation of think tank data resources into data assets, upgrading think tank data management to data governance, and advancing the intelligent development of think tank decision-making and consulting mechanisms.

[Purpose/Significanc] Scientific and technological innovation is founded on a system of knowledge in a field that is not formed in a short period but is constantly formed, evolved, and developed under the impetus of scientific research, resulting in the role transition of scientific knowledge. Revealing the phenomenon of scientific knowledge role transition contributes to revealing the intrinsic traits and principles of knowledge innovation. [Method/Process] The paper utilized the biomedical domain knowledge system's evolution record as its object, splitting down the process of scientific knowledge evolution into two stages with the domain knowledge system as the reference: the absorption of new knowledge and the evolution of the domain knowledge system. From two perspectives of the external attributes of individual knowledge and the internal genealogical relationships of the knowledge system, this paper explored two questions, "How do scientific knowledge individuals enter the domain knowledge system and become domain knowledge through growth and evolution?" and "How do changes in relationships within knowledge systems in turn affect the role of domain knowledge individuals within them?". [Result/Conclusion] Adoption and citation frequency in the new knowledge absorption phase positively promotes the transition of scientific knowledge, with adoption frequency also having a positive effect on the transition duration.In the evolution phase of the domain knowledge system, citation frequency, journal influence, and number of location tiers positively influence the role change of domain knowledge, although the impact intensity varies across the three types of role transition.

[Purpose/Significance] In order to help users quickly obtain the required information from the vast amount of social media text, this study innovatively designs an automatic summarization model based on the filtering attention mechanism, known as the Filter Unit Model (FUM). [Method/Process] Firstly, fine-tuned BERT was employed to perform vector embedding for the input social media text. Subsequently, the filtering attention mechanism was designed to eliminate redundant information in social media text, two types of filtering attention mechanisms, one operating at the word level and the other at the sentence level, both aimed at filtering the embedding vectors from diverse perspectives. Finally, the decoder part of the Transformer was used for decoding, and corresponding decoding strategies were designed according to the different filtering attention mechanisms. [Results/Conclusion] This study conducts comparative experiments with classic and excellent baseline models in the field of summarization on a microblog dataset. Experimental results show that the FUM model designed in this study outperforms other baseline methods. Additionally, it is found that the sentence-level filtering attention mechanism has a better filtering effect than the word-level one.

[Purpose/Significance] In the era of smart media, it is of great significance to achieve accurate classification of users' sentiment based on neural network model and deeply explores the potential value of massive text information. [Method/Process] Aiming at the problem of the limited effect of sentiment classification caused by strong dependence between layers of existing hybrid models and insufficient reflection of the importance difference of output features and so on, the paper proposed a hybrid neural network sentiment classification model with frequency domain enhanced self-attention mechanism based on the Stacking integration algorithm. Firstly, the paper constructed the parallel feature extraction base-learner layer combining with Bert, TextCNN, BiLSTM models. Secondly, the paper constructed the meta-learner layer incorporating the frequency domain enhanced self-attention mechanism. Thirdly, the paper fused the two layers based on Stacking algorithm, and combining word embedding layer and fully connected layer, systematically mine the deep semantic information as well as the local and global features, and then through the distribution of weight and discrete Fourier transform to improve the effect of sentiment classification. [Result/Conclusion] The results of comparative experiments and ablation experiments on the hotel review dataset show that the Bert-TextCNN-BiLSTM-FAttention sentiment classification model has a significant advantage over other models, and its accuracy, recall, and F1 value reach 91. 7%, 95. 3%, and 93. 9%, respectively. Besides, with the increase in the number of Epoch training rounds, the sentiment classification accuracy of the model continues to improve, and the loss value continues to decrease, which indicates that the model has a strong generalization ability.

[Purpose/Significance] As an emerging health risk in the information age, Internet health anxiety has attracted increasing attention in academia. Migrant workers are a vulnerable group to this anxiety. There has been no researches to explain the causes of Internet health anxiety among migrant workers from the perspective of online information seeking behavior. This paper aims to explore how online health information seeking behavior among migrant workers influences on their formation of Internet health anxiety. [Method/Process] This study integrated the health belief model (HBM) and the information search process (ISP) model and introduced new variables such as intolerance of uncertainty and health information quality. It constructed an impact factors model of migrant workers' Internet health anxiety based on online health information seeking. Data was collected through a survey questionnaire, and empirical testing on the model was conducted. [Result/Conclusion] Migrant workers prefer to use informal sources to search for health information. Health information seeking behavior has a significant positive impact on Internet health anxiety, specifically manifested in seeking frequency and strategy. Online health information seeking behavior affects Internet health anxiety by influencing health beliefs and emotions. Perceived health risks and symptom self-assessment partially mediate the relationship between health information seeking behavior and Internet health anxiety. Family and friends illness and psychological resilience moderate the mediating effect of health beliefs in the relationship between online health information seeking behavior and Internet health anxiety.

[Purpose/Significance] Infant and toddler online health information is becoming an important support for infant and toddler parenting, this paper explores the behavioral pathways of infant and toddler online health information adoption of the new generation parents, and provides an informational perspective and theoretical framework for the understanding and analysis of Chinese parenting practice. [Method/Process] A semi-structured interview method was used to collect the behavioral practice experiences of 32 new generation parents on infant and toddler online health information. Based on the grounded theory, eight main categories of context, motivation, information, subject, family, society, usefulness perception and response were sorted out, and then a theoretical model of the behavioral path of relevant online health information adoption was constructed. [Result/Conclusion] It is found that under different circumstances, parents acquire, identify and adopt relevant infants and toddlers' online health information based on different motivations, so as to further strengthen their parenting views and practice. In this process, family and society, as an external system, influence the behavior and practice of the new generation parents.

[Purpose/Significance] The differences-in-differences method is used to evaluate the innovation effect generated by big data policies, which provides a scientific basis for the improvement of big data policies and promoting the coordinated development of regional innovation capabilities. [Method/Process] Based on grounded theory, this study constructed a mechanism model of how big data policies affect regional innovation capabilities. On this basis, with the balanced panel data derived from 31 provinces and cities across China (excluding Hong Kong, Macao, and Taiwan) from 2010 to 2020, this study used the differences-in-differences method for empirical testing. [Result/Conclusion] The big data policies can significantly promote the improvement of regional innovation capabilities, as verified by robustness tests. Mechanism examination reveals that big data policies facilitate the enhancement of regional innovation capabilities through four pathways: digital drive, industry agglomeration, talent agglomeration, and open cooperation. Moreover, this promotion effect notably varies depending on the difference in geographical region, dimensions of regional innovation capability, and levels of regional innovation capability development. Furthermore, big data policies have a more significant innovation-driving effect in the eastern and central regions, in the innovation output dimension, and in areas with high-level regional innovation capabilities.

[Purpose/Significance] Intelligent services have become an important direction for the transformation and development of university libraries. Natural language processing technologies empower the intellectualization of university library services, reconstructing service models and processes, and helping improve the overall service levels of university libraries. Named entity recognition is an important task in natural language processing, its impacts and significance on the intellectualization of library services can effectively identify entities such as people, locations, organizations, resource utilization, service features, and cultural promotion in library information, providing support for knowledge organization, information retrieval, etc. [Method/Process] This study analyzed the application prospects of named entity recognition technology in the intelligent service systems of university libraries. By constructing a high-quality library and information science corpus, it provided high-quality training data to meet the specific entity recognition needs within the field, enhancing accuracy and adaptability, and laying the foundation for optimizing library intelligent service systems. An ALBERT-BiLSTM-CRF model based on deep learning was adopted to verify the effects of named entity recognition tasks. Case studies on service recommendations and knowledge graphs in university libraries were performed and compared with existing mainstream large language models at home and abroad. [Result/Conclusion] The results show that the proposed method effectively improves the performance of named entity recognition in the university library information field, which is conducive to the promotion and application of intellectualized library services, while also reducing resource waste and training costs. In addition, this paper explores the possibility of LibraryGPT, a large language model serving the library field, in order to provide references and inspiration for the promotion and development of smart services in university libraries in the future.

[Purpose/Significance] From the perspective of risk communication, the identification of the constituent elements of the emergency convergence media information ecosystem, the causal sequence and criticality of the elements are helpful to optimize the system in an orderly and hierarchical manner, and help the emergency response work. [Method/Process] This paper discussed the structural framework of the emergency convergence media information ecosystem, identified its constituent elements, calculated the "four degrees" of each component by using the Fuzzy Set Theory—DEMATEL research method, discussed the causal order and criticality of the constituent elements of the emergency convergence media information ecosystem, and conducted cluster analysis on this basis. [Result/Conclusion] The study identifies 17 cause elements and 21 effect elements among the 38 constituent elements, and 14 key elements among the constituent elements, and the cluster analysis divided the constituent elements into six levels. The research results provide a reference for the orderly and hierarchical optimization of the emergency convergence media information ecosystem.

[Purpose/Significance] As the main force of basic research and the source of major scientific and technological breakthroughs in China, universities are an important supporting force for the country's self-reliance and self-improvement in science and technology. With the gradual shift of scientific research paradigm from individual free exploration to organized big science, the problems arising from the deepening of the degree of organizational research in universities have also gradually attracted attention from the academic community. [Method/Process] This article adopted the grounded theory method and constructed a model of the influencing factors of organized scientific research in universities through a coding process of extraction and summarization. It deeply analyzed the influencing factors and mechanisms of organized scientific research in universities. [Result/Conclusion] The study found that institutional factors, humanistic factors, and value factors mainly affect the effectiveness of organized scientific research in universities. Among them, institutional factors such as top-level design, mechanism system, and research conditions are important external guarantee factors that affect the organized scientific research activities of universities; Factors such as ideological identification and team building are important internal driving factors; The transformation of technological innovation achievements into economic and social value can have a demonstration effect. Based on the above analysis, this article explores the implementation path of continuously improving the effectiveness of organized scientific research development in universities from the aspects of strengthening top-level design, deepening institutional and mechanism reform, adhering to people-oriented, improving talent team construction, focusing on achievement transformation, and facilitating transfer and transformation channels.

[Purpose/Significance] As an important driving force for the innovative development of humanities and social sciences, Interdisciplinary/Integrated Studies is an inevitable way to solve complex problems. As an important carrier of cross-research, the Interdisciplinary/Integrated Research Program in Humanities and Social Sciences of the Ministry of Education has played an active role in promoting cross-research in humanities and social sciences in recent years, and aims to provide useful insights and references for future cross-research in humanities and social sciences through the research. [Method/Process] This study systematically analyzed 4045 Interdisciplinary/Integrated research projects funded by the Ministry of Education (MOE)over the past decade(2014—2023)using a combination of quantitative and qualitative methods to explore the number of projects, types, their distribution, research topics, and trend. [Result/Conclusion] The study found that interdisciplinary research in the humanities and social sciences has grown steadily in both quantity and depth, and the research topics cover a wide range. Important research hotspots have emerged in Urban and rural social development and special group issues, environmental protection and sustainable development, regional economy and industrial transformation and upgrading, cultural heritage and innovative development, artificial intelligence and information technology application, mental health and educational innovation. It also introduces the integration of multiple disciplines, combining empirical research and theoretical exploration, focusing on real problems, and giving equal importance to innovation and practicality as the characteristics of cross-cutting research.

[Purpose/Significance] College students' innovation and entrepreneurship team is a new force in China's "mass entrepreneurship and innovation" policy. In recent years, the interdisciplinarity feature of team members has become increasingly prominent. Exploring the influencing factors of interdisciplinary knowledge integration effect can help clarify the improvement path of integration effect, and improve team innovation performance. [Method/Process] According to Wuli-Shili-Renli (WSR) system method ology and literature research, the study extracted the factors that may affect the interdisciplinary knowledge integration effect of college students' innovation and entrepreneurship teams from three aspects, namely, knowledge integration object, process and subject. Then it collected 343 valid data through questionnaire survey, and conducted empirical analysis of influencing factor path and antecedent configuration through structural equation model and fuzzy set qualitative comparative analysis. [Result/Conclusion] Tacit knowledge, interdisciplinary communication intensity, interdisciplinary task interdependence, opinion leader coordination, epistemic stability, epistemic adaptability, emotional trust, intrinsic motivation are significant influencing factors. There are two antecedent configurations that affect the integration of interdisciplinary knowledge, namely, internal drive type and interdisciplinary value co-creation type. And the explanatory power of interdisciplinary value co-creation type is much greater than that of internal driven type.