［Purpose/significance］ The renaming of the first-level discipline has brought brand new opportunities and challenges,and analyzing the current situation and feasible development approaches of information science is of great practical significance to the future construction and development of the discipline of information science.［Method/process］ This paper reviews the development and transcendence of the information resource management school of Chinese information science,analyzes the internal and external development dilemmas of Chinese information science in the context of the renaming of the first-level discipline based on the analysis of changes in the disciplinary affiliation,the competition between the related disciplines and the expansion of the discipline,and the differences in the paradigm of the different schools of information science,and combined with the current development of the discipline to explore the future development path of Chinese information science in the framework of the first-level discipline of information resource management.［Result/conclusion］ The research results indicate that the development of Chinese information science under the framework of the first-level discipline of information resource management needs to focus on the intrinsic logical connection between the second-level disciplines,i.e.,DIKW resource management,to expand the different basic theory system of information resource management,and around the logical chain of expanding the new,guarding the correctness,and integrating the common advancement,to promote the information science as a kind of management discipline based on information resources,to carry out the education of weakly sensitive intelligence literacy integrated with the training of intelligence thinking,and to speed up the construction of a comprehensive knowledge system and methodology system of information science.

［Purpose/significance］ Open Public Data,as a vital component of public data,is critical for effective utilization.Analyzing the mechanisms for releasing the application value of Open Public Data can enhance its effective use.［Method/process］ Based on the SOR framework,this study treats application scenarios as stimuli and analyzes their connotations and attributes.The paper then identifies the key participants on both the supply and demand sides of Open Public Data,explores their behavioral responses through case studies,and proposes mechanisms and pathways for realizing the value of Open Public Data driven by application scenarios.［Result/conclusion］ The study concludes that the application-scenario-driven value release mechanism of Open Public Data comprises five fundamental aspects,namely:scenario objectives,demands,collaboration,application,and iteration.The pathways for value realization include enhancing data demand management,refining data openness policies,optimizing the development of open data platforms,integrating technological solutions into practice,and establishing mechanisms for feedback and evaluation.

［Purpose/significance］ Assessing the maturity of scientific research big data governance is crucial,yet the lack of evaluation theory and practice exacerbates issues such as fragmentation,dormancy,and hibernation within this field.This paper endeavors to construct a maturity assessment model for scientific research big data governance through a data ecology perspective and refine the evaluation method.［Method/process］ By leveraging data ecology theory,a data model is proposed,leading to the development of the (maturity model of scientific research big data governance based on data ecology,MM-SRBDG).The MM-SRBDG model comprises 8 core domains,29 capability domains,and 101 process domains,alongside an indicator system.Additionally,a comprehensive evaluation method,E-C-A,is formulated,followed by practical case-based assessment and systematic analysis.［Result/conclusion］ The MM-SRBDG model systematically delineates the constituent dimensions of scientific research big data governance maturity,offering a new perspective for systematic governance in this domain.The constructed indicator system and evaluation matrix exhibit systematic and in-depth characteristics.The E-C-A method,tailored to diverse dimensions,furnishes both theoretical grounding and practical guidance for assessing scientific research big data governance maturity,thereby enhancing governance standards.

［Purpose/significance］ Exploring the orchestration behavior and value creation mechanisms in the context of data scenarios,providing new insights for data management and the valorization.［Method/process］ Based on the characteristics of data and data scenarios,the traditional orchestration theory model is improved.A data orchestration theoretical theory model is proposed,which includes three modules:data resource management,data asset orchestration,and data capital operation,along with related processes.According to the evolution path of data form of resourceization,assetization and capitalization,a detailed analysis is conducted on the role of each data orchestration module and process in the valorization of data.［Result/conclusion］ Constructing a theoretical framework for data orchestration to expand the thinking of data management,and proposing a new path for the realization of data valorization from the perspective of micro-subject behaviors.

［Purpose/significance］ The ongoing evolution of the information environment,technological environment and scientific research paradigm is driving profound changes in the industry pattern,form and method of knowledge services.An investigation into the evolution of knowledge service models in the context of AI for Science (AI4S) holds significant value for guiding the development strategies and directions of knowledge services in the new era.［Method/process］ This article delves into the policy strategies of AI4S and the development trends in academia and industry,clarifying the significant impact of AI4S on the transformation of knowledge service industries,as well as the new challenges and tasks in supporting AI4S.Taking the practical exploration of the National Agricultural Library as an example,we propose corresponding strategies from three dimensions:the construction of knowledge resource foundations,the shaping of intelligent computing capabilities,and the development of service product systems.［Result/conclusion］ Within the AI4S paradigm,knowledge services are confronted with new demands for intelligence,personalization,and precision.Key strategies for adapting to technological advancements and business reconfiguration include reinforcing the foundational knowledge resources,enhancing capabilities for knowledge extraction,developing competencies for AI applications,and establishing new collaborative research platforms.

［Purpose/significance］ LLMs,with their powerful natural language processing capabilities,have played an increasingly important role in the field of crisis informatics in recent years.This study proposes and constructs a comprehensive evaluation benchmark,CIEval,to scientifically evaluate the crisis information generation abilities of LLMs.［Method/process］ This study first constructed CIEval,a comprehensive evaluation dataset covering twenty-six crisis scenarios such as natural disasters,accident disasters,public health events,and social security events.Eight LLMs with Chinese processing capabilities were selected for evaluation,and multidimensional criteria,including content quality,expression,feasibility,and utility,were established for information generation.A combination of manual and machine scoring methods was used to assess each model.［Result/conclusion］ The results show that Claude 3.5 Sonnet outperforms other models in crisis information generation tasks,especially when dealing with complex and variable natural disasters and accident disasters.The information it generates is comprehensive and highly practical.In contrast,domestic models like ERNIE 4.0 Turbo and iFlytek Spark V4.0 have slightly lower overall scores than top international models but still perform exceptionally well in specific crisis scenarios.Regarding this,emergency departments can select appropriate LLMs for information generation based on specific crisis types to better respond to emergencies.

［Purpose/significance］ Explore the working framework of predictive intelligence on the security risks of generative artificial intelligence,so as to provide references and inspirations for China’s intelligence agencies.［Method/process］ Through literature review to understand the current research status of the perception of the security risks of generative artificial intelligence,summarize the development trends of the security risks of generative artificial intelligence,and analyze the passivity of the current risk governance of generative artificial intelligence.On this basis,discuss the relevant mechanisms and processes of predictive intelligence work,and construct an exploratory framework for the perception of the security risks of generative artificial intelligence.［Result/conclusion］ The embedding of predictive intelligence work into the perception of the security risks of generative artificial intelligence is a forward-looking strategy.Its embedding mechanism involves multiple interrelated levels.Universality,dynamism,and predictability,as the key supporting elements,can effectively cope with the current trends and evolutionary characteristics of the security risks of generative artificial intelligence,make up for the insufficient perception of non-linear emergencies in traditional intelligence,and thus improve the passive situation of the current governance of the security risks of generative AI.

［Purpose/significance］ According to the error theory,measurement error is divided into systematic error and accidental error,which is extended to principal component and factor analysis,and it is of great significance to study the influence of error on the academic evaluation of the index system.［Method/process］ On the basis of theoretical analysis,the formation mechanism of principal component and factor analysis system error is deeply analyzed,and the test and measurement methods are discussed.［Result/conclusion］ Taking CNKI environmental science and technology journals as an example,the results show that there are systematic errors in principal component analysis and factor analysis.The links that generate systematic errors in principal component analysis and factor analysis are diverse,including the selection of evaluation indicators,the standardization method of reverse indicators,the determination of the explanatory power of principal components or public factors,the data loss caused by the selection of a few principal components or public factors,the weight setting,the evaluation value publishing method,etc.The systematic error of principal component analysis and factor analysis can only be measured appropriately.Comprehensive measures should be taken to reduce the systematic error of principal component analysis and factor analysis.

［Purpose/significance］In the era of multidisciplinary integration,analyzing the status of each discipline in the scientific system is not only conducive to grasping the current status of disciplinary development but also advancing the overall layout of scientific development steadily.［Method/process］ The knowledge flow in interdisciplinary citation network is similar to the flow of an ideal fluid:it flows from areas with high potential energy to areas with low potential energy,promoting the increase of potential energy and the development of low-potential energy disciplines.Based on the mutual relationship between interdisciplinary literature,this paper draws on the potential energy theorem of fluid mechanics to construct a disciplinary potential energy model.Empirical research was conducted using 30-year citation data from 98 disciplines.Based on the calculation results of the model,citation potential energy networks of the scientific system in three different periods of each decade were drawn to analyze the current status of knowledge flow and status evolution of each discipline.［Result/conclusion］At the macro level,the exchange between disciplines in the scientific system is becoming increasingly close,and the status of social science disciplines is getting higher and higher.At the meso level,the interdisciplinary knowledge exchange groups formed by the 98 disciplinary exchange clusters have merged from initial 6 to the recent 4.At the micro level,environmental science and engineering,agricultural engineering and business administration have always occupied a prominent position in the citation potential network.Recently,the status of Marxist theory,geography,and theoretical economics has shown a clear upward trend.

［Purpose/significance］ Exploring the influencing factors and mechanism of open science maturity of research institutions is of great significance for improving the development level of open science.［Method/process］ Based on grounded theory analysis and TOE theory,a model of influencing factors of open science maturity of research institutions was constructed,and empirical analysis was carried out by 322 questionnaire survey and structural equation model.［Result/conclusion］ It is found that institutional factors,infrastructure factors,organizational factors,technological factors,collaborative factors,personnel factors have significant direct and positive effects on the open science maturity of research institutions.In addition,organizational factors,personnel factors,infrastructure factors and technological factors play mediating roles between collaborative factors and open science maturity,while infrastructure factors and technological factors mediate the relationship between institutional factors and open science maturity.［Limitations］ In the future,research institutions can improve the level of open science in six aspects:establishing and improving open science system,promoting construction of open science infrastructure,optimizing organizational management of open science,strengthening development and application of open science technologies,promoting multi-subject collaboration in open science,and increasing institutional personnel support.

［Purpose/significance］ The research focuses on the construction of knowledge service co-creation system of smart library under the “cloud digital intelligence” integration paradigm,aiming to reveal the transformation and efficiency improvement of knowledge service mode of university library under the “cloud digital intelligence” innovation ecology by integrating the theory of value co creation elements and synergy theory.［Method/process］ The research examined the value co creation foundation,implementation path,interaction relationship and influencing factors of the knowledge service of Smart Library,constructed the value co creation system model,and proposed the operation mechanism and strategy optimization scheme to enhance the core competitiveness of higher education institutions,optimize service efficiency,and stimulate innovation ability.［Result/conclusion］ As the core node of knowledge co-creation ecosystem,smart library needs to pay attention to system construction and service innovation,rely on multi-agent cooperation to promote the steady and sustainable development of knowledge service ecosystem,and provide theoretical support and practical guidance for the construction of smart library 4.0.

［Purpose/significance］ Through the linkage and fusion of multimodal online public opinion data,it provides comprehensive data support for public opinion analysis and assists in making complex public opinion decisions.［Method/process］ Firstly,we comprehensively analyze the elements and semantic relations of network public opinion knowledge,design the network public opinion ontology model OPO,and standardize the concept and attribute of network public opinion domain knowledge.Then,we use the associated data technology,through the entity naming,entity RDF,entity association,associated data storage and release and other key steps,to build the network public opinion associated data set.Finally,we set up 4 evaluation dimensions and 23 specific indicators based on the existing evaluation practice.［Result/conclusion］ Taking earthquake public opinion as an example,we carried out the experiment of creating and evaluating network public opinion related data set.The results show that the application of linked data in the field of network public opinion can reveal the internal relationship between data,promote the multi-dimensional fusion of multi-source heterogeneous data,and provide data support for the intelligent analysis of public opinion.

［Purpose/significance］ This study aims to employ network analysis methods to study the critical roles of information broker nodes play in bridging different voice and exchanging various opinions in social media.［Method/process］ Using interaction data from the social media platform “Twitter,” this study visualizes opinion polarization between two political parties and proposes an improved betweenness centrality algorithm.Nodes with high betweenness centrality are identified as invisible key broker nodes within polarized networks.The SI model is employed to simulate the information diffusion process in socially polarized networks,exploring the vital role of these key broker nodes in information dissemination.［Result/conclusion］ The results indicate that removing broker nodes significantly reduces the speed of information dissemination in polarized networks.This effect is particularly pronounced in cross-community dissemination,where both the scope and efficiency of information spread are severely impacted.These findings demonstrate that broker nodes,acting as bridge nodes connecting different communities,significantly enhance the efficiency of cross-community information dissemination by filling structural holes between communities.

［Purpose/significance］ A multi- task user needs identification model integrating disease characteristics (MUNI-DC) is proposed for user generated content in online health communities.Deeply explore user needs and form a demand theme system consisting of two parts:questioning intention and questioning entity.［Method/process］ By constructing a BERT-wwm model for pre-training user demand data and integrating user medical condition characteristics to achieve recognition of user intentions;Furthermore,a multi-layer label pointer network is used to achieve entity recognition of user query data in online health communities.Based on this,identify the needs of users in online health communities.［Result/conclusion］ Comparative experiments have shown that compared to a single task model,this model has improved in precision,recall,F1,and other indicators of user requirement recognition results.The ablation experiment found that the fusion of disease characteristics and multi-layer label pointer network can effectively improve the user demand recognition performance of the model.The proposed MUNI-DC model has reference value in dealing with online health community user information demand analysis tasks.

［Purpose/significance］ In social networks,the effective identification of high-influence users is of great practical significance for network public opinion control and network ecological governance.Aiming at the problem that the existing social user influence calculation ignores users’ emotional feedback transformation and interactive evolution,a new social user influence measurement method is proposed.［Method/process］ Firstly,based on statistical analysis,the influence of emotional feedback transformation and interactive evolution on user influence is examined.Secondly,the emotional feedback of users is analyzed from the time dimension,and the emotional feedback value is revised according to the feedback tendency and transformation rate.Then,according to the connection between the network structure of different time windows and the passage of time,the interactive evolution measurement method based on “reward and punishment-decay” is proposed.Finally,this paper integrates the emotional feedback transformation and interactive evolution into the computation of user influence,thereby enabling a dynamic assessment of social user influence.［Result/conclusion］ The experimental results show that compared with the existing social user influence measurement methods,the new method has certain improvements in the precision,recall and F1 value.

［Purpose/significance］ From the perspective of fine-grained semantics,this study explores the extraction of research problems and methods and the identification of domain knowledge flow states to trace the knowledge evolution in research fields and grasp the development trends of domain knowledge.［Method/process］ The study validates the effectiveness of entity extraction using GPT prompt learning and examines its application in entity extraction.An innovative method is proposed for identifying key entities based on eigenvector centrality and Z-score.Additionally,a quantitative framework for domain knowledge flows is introduced,along with the categorization and identification methods for knowledge flow states,enabling in-depth analysis of domain knowledge flows.［Result/conclusion］ The domain knowledge flow states are categorized into five types:knowledge emergence,knowledge obsolescence,knowledge inheritance,knowledge merging,and knowledge splitting.This approach achieves effective identification and characterization of domain knowledge themes.The findings demonstrate that the proposed method effectively reveals the knowledge evolution in the field of artificial intelligence,making it a powerful tool for domain profiling.

［Purpose/significance］ With the change in the operating environment of knowledge organization systems,the importance of knowledge organization continues to increase.In order to break through the traditional thesaurus construction and application dilemmas,this paper explores a new paradigm for thesaurus construction by integrating the latest large language model technology.［Method/process］ Starting with the characteristics of the thesaurus itself and its construction approach,the study adopts a full-process fine-tuning strategy that includes continued pre-training,supervised fine-tuning,and reinforcement learning combined with a local knowledge base to fine-tune the large language model.Empirical studies are conducted in the fields of “Quantum Technology” and “Theoretical Mechanics”.［Result/conclusion］ The empirical findings show that the fine-tuning scheme with continued pre-training,“multi-strategy data processing fine-tuning”, and Reinforcement Learning from Human Feedback (RLHF) performs better.Specifically,the accuracy of constructing hierarchical relationships in the existing thesaurus for the “Theoretical Mechanics” field reaches 89.06%,while for the emerging field of “Quantum Technology”，the accuracy is 63.02%.This indicates that the proposed scheme can effectively construct hierarchical relationships in existing thesauri and performs well in the construction of hierarchical relationships for thesauri in new fields,demonstrating its feasibility and providing a reference for the construction of thesauri in emerging areas.

［Purpose/significance］ In previous studies on financial sentiment analysis,most research has been limited to analyzing individual comment texts,and the extraction of sentiment elements in aspect-based financial sentiment analysis has often been insufficiently comprehensive.To address these issues,we propose a new task of aspect-based sentiment analysis of conversational stock forum comments.［Method/process］ Based on this task,we construct a two-stage framework for aspect sentiment quadruple prediction in conversational stock forum comments.The “DiaASQ+RoBERTa” method was applied to sequentially extract aspect categories,aspect terms,opinion terms,and sentiment polarities from the comments.Aspect sentiment quadruples were then obtained through a combination and mapping of these sentiment elements.An empirical study was conducted on a dataset of conversational stock forum comments derived from the Eastmoney Stock Forum.［Result/conclusion］ Experimental results demonstrate that the “DiaASQ+RoBERTa” method achieved approximately a 16.18% improvement in F1 score over the best baseline model on the same dataset,highlighting its superior performance.This research not only expands the scope of sentiment analysis in the financial domain but also enhances the richness of aspect-based sentiment element extraction,providing more comprehensive references for investment decision-making.

［Purpose/significance］ Disruptive technologies can change the existing technological landscape,which may create a new industry or change the existing industry,and major countries in the world are laying out research on disruptive technologies.However,due to the uncertainty and complexity of disruptive technologies,it is difficult to identify them,especially in the early stage,therefore,it is crucial to systematically sort out the early identification methods of disruptive technologies.［Method/process］ In this paper,through the method of literature review,103 literatures related to the early identification features of disruptive technologies are systematically sorted out,and then the early identification features are extracted from them,and the measurement methods of the corresponding features are summarised.［Result/conclusion］ Through the analysis and summary,4 identification perspectives,10 early identification features and the corresponding calculation methods are finally summarised,of which the top three features are novelty,uncertainty,and cutting-edge in order;it is found that when exploring the essential features of disruptive technologies based on weak signal perception,the combination of weak signals and disruptive technology characteristics needs to be paid attention to;the existing identification indexes pay more attention to the hotspot monitoring instead of early identification;the current identification features focus on hot spot monitoring instead of early identification;and the current identification features have been used in a variety of ways rather than early identification;the current identification feature indicators are insufficient in mining and analysing weak signals.

［Purpose/significance］ Through content analysis and in-depth exploration of the United States’ international cyberspace and digital policies,provide inspiration and reference for the development,response,and breakthrough of China’s international cyberspace and digital policies.［Method/process］ This article adopts the methods of online survey,literature research,and qualitative text analysis to sort out and analyze the relevant content of the US international cyberspace and digital policies from top to bottom.Through text analysis and coding,a category system is constructed to extract core strategic elements.［Result/conclusion］This study found that the United States has integrated cyberspace and digital policies into its diplomatic level,reflecting a profound adjustment in its management thinking,which may deepen China’s passive position and uncertainty in the international community.Based on this,this article is based on China’s national conditions,combined with the strategic elements of the United States’ international cyberspace and digital policies,focusing on the logic of breaking through the cyberspace,the strategic layout of digital policies,and the strategy of science and technology diplomacy,to propose optimization directions and countermeasures for China’s international cyberspace and digital policy layout.

［Purpose/significance］ Summarize the EU data cross-border risk identification and control experience,and provide reference for the construction of China’s data cross-border risk identification and prevention and control system.［Method/process］ Based on the evidence-based perspective,we systematically sort out the data cross-border cases disclosed by the EU,so as to establish clues for identifying data cross-border risks,analyze the organizational structure and operational mechanism of risk control in the EU,and then focus on analyzing the experiences and laws of the EU’s data cross-border risk review and research and judgment,and summarize the insights for China’s data cross-border governance.［Result/conclusion］The EU has set up a top-down collaborative organizational structure and constructed a relatively perfect data protection impact assessment,records of processing activities,data subject access request,and notification of data breach to achieve the review and control of risks.China needs to learn from the practical experience of the EU to achieve precise control of risks in three aspects:precision,coordination and transparency,strengthen inter-agency coordination and cooperation,enhance the transparency of the cross-border data review process to improve the existing mechanism for reviewing cross-border risks of data.