[Purpose/Significance] To explore the influence mechanism of Chinese traditional cultural elements on the user experience of video games, provide theoretical references and practical strategies for the integration of traditional culture into video game design, and further explore the effective paths for the contemporary communication of traditional culture among young people. [Process/Method] Taking Black Myth: Wukong as the research object, this study integrated theories of the technical communication of the traditional culture, symbolic interaction, and user experience. An experimental study was conducted using eye-tracking technology, where participants completed the prologue and combat events of the game. Eye-tracking metrics, such as the fixation count and total fixation duration in four areas of interest (characters, landscapes, combat, and costumes) were collected. Correlation and regression analyses were performed by combining data from a traditional cultural element perception questionnaire and the Game Experience Questionnaire (GEQ). [Result/Conclusion] Visual symbols of characters and costumes dominate the sensory experience. Dynamic symbols of characters, landscapes, and combat drive the interactive experience, while the cultural decoding of combat symbols triggers the value experience. Fixation metrics related to costumes and combat have significant explanatory power for users’ perceived competence and jointly form the basis of the immersive experience. The study also reveals the underlying reasons for the lack of a significant correlation among flow, emotional dimensions, and eye-tracking metrics. The findings provide theoretical references and practical strategies for integrating traditional Chinese cultural elements into video game design.

[Purpose/Significance] To overcome the limitations of traditional static topic analysis, which insufficiently considers dynamics and industrial relevance when depicting the evolution of emerging technologies, and to address the survivorship bias caused by retrospective analyses based on global data, this study integrates a three-dimensional perspective of “industrial chain-technology topic-dynamic evolution” to propose a method for identifying and analyzing dynamic emerging technology topics oriented toward industrial chains. Through an empirical analysis of patent data from the Beijing hydrogen energy industry (2010–2024), this paper verifies the effectiveness of a forward-looking dynamic technology topic identification framework, offering theoretical and practical references for policy-making, resource allocation, and cross-industry method transfer. [Method/Process] This study develops a dynamic emerging technology topic identification framework that integrates an industrial chain perspective, comprising topic identification and optimization, industrial chain construction, and the mapping of topics to chain segments. The research innovatively introduces two dynamic observation methods—forward-looking (ex-ante) and backward-looking (ex-post) perspectives—corresponding to evolutionary tuning and global tuning strategies, respectively. Using a topic similarity network, this study constructs intertemporal evolutionary paths and utilizes river charts to visualize the temporal evolution characteristics of technological topics. A three-dimensional analysis is conducted: evolutionary pattern identification, stage division, and key topic identification. [Result/Conclusion] Empirical findings reveal that the evolution of Beijing hydrogen energy technology has experienced three phases: technology dispersion, technology integration, and system integration. Two typical evolutionary patterns, continuous and disruptive evolution, are identified. Key topics such as “Chemical Separation and Reforming Technology” and “Hydrogen Energy System Integration Technology” form the core technological innovation pathways, while topics like “Hydrogen Energy Drone System Technology” demonstrate leapfrogging breakthrough characteristics. Technological topics exhibit a diversified distribution of upstream hydrogen production technologies, a relatively concentrated midstream storage and transportation sector, and a dense downstream application segment, reflecting the industry’s trend toward integration and large-scale development. The constructed framework, which combines forward-looking and backward-looking perspectives, has significant advantages in avoiding survivorship bias and capturing the uncertainties of technological evolution, providing a systematic and reusable analytical tool for the emerging industry technology evolution.

[Purpose/Significance] Technology convergence continuously generates new technological trajectories through cross-domain knowledge recombination and has become a critical mechanism driving industrial upgrading and technological paradigm shifts. Systematically identifying convergent technology combinations with high development potential is of substantial theoretical and practical significance for forward-looking technology opportunity discovery and the strategic allocation of innovation resources. [Method/Process] Based on the fundamental characteristics of technology convergence, this study develops a multidimensional evaluation framework from the perspectives of technology fusion and technology novelty. Technology combinations constructed from IPC co-occurrence at the patent level are used as the analytical units. An entropy-based weighting method is applied to determine the relative importance of the core dimensions, forming a quantitative model for assessing convergence potential. An empirical analysis is conducted using patents in the autonomous driving domain. [Result/Conclusion] The findings reveal a clear synergistic effect between technology fusion and technology novelty in the evolutionary process of convergent technology combinations. The integrated evaluation results effectively differentiate technology combinations across different potential levels, with high-potential convergent technology combinations exhibiting stronger technological diffusion capabilities.

[Purpose/Significance] In response to the key issues such as weak reproducibility and difficulty in multi-modal response assessment in the current evaluation of government intelligent Q&A systems, this paper, based on the benchmark testing concept, integrates large language models and machine learning technologies to construct a set of standardized evaluation question sets and an automated assessment framework applicable to government affairs scenarios, in order to support the scientific quantification and continuous optimization of system performance. [Method/Process] By systematically collecting and cleaning government affairs hot issues, a high-quality initial question set was formed; using an automated process to obtain the real responses of representative government intelligent Q&A systems and integrating the semantic understanding of large language models with traditional feature extraction methods, a set of answer keywords that can be used for automated judgment was generated; finally, a standardized evaluation question bank covering government affairs topics in multiple fields was constructed, and batch testing and quantitative evaluation were conducted on provincial-level government intelligent Q&A systems in China. [Result/Conclusion] The experimental results show that the automated evaluation method proposed in this paper has a significant consistency with the manual evaluation results, with a rank correlation coefficient of 0.77. Based on 10 rounds of actual tests and regression extrapolation, the dispersion of evaluation results can be kept within 5% over up to 27 rounds, demonstrating good robustness and reproducibility. This benchmark testing method has strong universality and scalability, and can not only be used for horizontal comparison between systems but also effectively track the performance changes of systems during minor version iterations, providing a feasible evaluation tool and reference basis for improving the quality of online government question-answering services.

[Purpose/Significance] To solve problems such as semantic ambiguity and incomplete structure of user queries and improve the effectiveness of medical query intent recognition, an intent recognition method RRQ-LMIR based on LLM retrieval re-ranking and query optimization is proposed. [Method/Process] Firstly, a hybrid retrieval strategy is utilized to obtain candidate examples with semantics similar to the user’s original query from an external knowledge base. Then, structured prompt information is constructed to guide the LLM to re-rank the candidate results in terms of semantic relevance. Subsequently, based on the re-ranking results, the LLM is utilized to generate an optimized query with clearer semantics and a more complete structure. Finally, a fine-tuned deep learning model is adopted to classify the intentions of the initial query and the optimized query, and the final prediction result is obtained through an equal-weight fusion strategy. [Result/Conclusion] The experimental results show that the F1-score of the RRQ-LMIR method for medical query intent recognition constructed in this paper reaches 84.30%, which is 3.26% and 22.7% higher than those of the current mainstream deep learning model RoBERTa-wwm-ext and the large language model GPT-5, respectively. The method proposed in this paper effectively solves problems such as semantic ambiguity and incomplete structure, and improves the performance of medical query intent recognition.

[Purpose/Significance] This study aims to enhance large language models’ (LLMs) capability for knowledge extraction through a multi-stage strategy, addressing performance bottlenecks in the specialized biomedical contexts, and providing a replicable path for knowledge organization, extraction, and discovery in pharmacological field. [Method/Process] A knowledge organization framework covering 12 entity types and 26 relation types was constructed, and domain-specific corpora were generated via prompt engineering. The proposed three-stage strategy (Multi-KE), integrating weak supervision, strong supervised fine-tuning, and reflective optimization, progressively improved the model’s extraction performance and interpretability. Multi-hop path discovery based on a knowledge graph was conducted to evaluate the model’s applicability in uncovering drug mechanisms and potential indications. [Result/Conclusion] On the PubMed dataset, Multi-KE achieved F1 scores of 82.20% for entity extraction and 65.54% for relation extraction, significantly outperforming general large models (GPT-4o, LLaMA3-8B) with improvements of 20.32%, 34.53% and 29.47%, 47.97% respectively, and performing only slightly lower than DeepSeek-R1 (by 2.44% and 3.48%). Meanwhile, it achieved substantial improvements of 22.54% and 35.49% over the domain-specific large model OpenBioLLM-70B. Furthermore, the ADE-Corpus-V2 dataset was used to validate the model’s generalization ability, and ablation studies confirmed the contribution of the three-stage strategy to performance improvement. Through data distribution and error analysis, the study also explored in depth the model’s performance and limitations in low-frequency entity recognition and complex relation extraction.Traceable extraction records and reflective optimization enhanced the model’s transparency and interpretability. Furthermore, through multi-hop path analysis, the proposed framework is validated for its effectiveness in identifying potential drug–disease associations and supporting the analysis of complex pharmacological mechanisms.

[Purpose/Significance] Current methods for identifying disruptive technologies tend to focus on structural characteristics and lack fine-grained content knowledge. This study introduces and optimizes the knowledge gene method, and constructs a knowledge-gene-based Disruptiveness Index (GD) to enhance the accuracy and granularity of identification. [Method/Process] First, the knowledge gene extraction formula is improved to parse multiple knowledge gene entities from patent literature. Second, the GD index is calculated for each patent. Finally, an empirical analysis is conducted on patents in the solar photovoltaic field, comparing the results with traditional disruption innovation indices (D and DI⁵). [Result/Conclusion] Knowledge genes extracted in this paper cover key technologies such as battery materials, structural design, and multi-junction stacking technology in this domain. The GD index is more sensitive to technological breakthroughs than the D and DI⁵ indices, and it can effectively screen out a number of disruptive technologies and core innovation entities in the industry. The method of identifying disruptive technologies based on the GD index can reveal the disruptiveness of patents at a fine-grained knowledge level and has advantages in technology evaluation with strong knowledge intersection.

[Purpose/Significance] Constructing a correlation model that integrates structural features and semantic connotations to achieve accurate identification of fine-grained topics in knowledge domains, while revealing the dynamic evolution of the relationships among the intersectionality, burstness, and novelty of finegrained topics, providing theoretical and methodological support for the research foundation, development trends, and intrinsic driving forces of deep understanding of the field. [Method/Process] Firstly, based on the literature cocitation network, clustering structures are divided to identify macro topics. Secondly, based on the citation relevance, the cited literature of each clustering structure is selected, and the improved BERTopic topic model is used to identify fine-grained topics and classify cross topic papers into multiple topic categories. Reconstruct indicators of intersectionality, burstness, and novelty to achieve multidimensional features calculation for finegrained topics. Finally, a phased analysis of the intersectionality, burstness, and novelty indicators of each finegrained topic will be conducted to reveal the evolutionary laws of scientific research development. [Result/ Conclusion] Taking the blockchain field as an example for case study, the research show that: (1) the macro topics in the blockchain field are divided into six categories, and the extended citing literature topics are divided into 56 fine-grained topics, of which 7868 cross disciplinary papers are divided into different fine-grained topics; (2) The blockchain field is undergoing significant transformation and expansion, with cross disciplinary themes showing significant stability. The topics of emergence and novelty are changing significantly, and the breakthrough innovative topics with high values in all three indicators are blockchain+artificial intelligence and privacy protection; (3) The changes in the correlation among thematic intersectionality, burstness, and novelty indicators indicate that this field has undergone an evolution from being driven by foundational, paradigm level, and crossborder innovation in the early stages (strong coupling of indicators), to being driven by mid-term application/hype (coupling of burstness and novelty, decoupling from intersectionality), and then to the current diversified, specialized, and rational development (universal decoupling among indicators). [Innovation/Value] The main innovations of this paper can be summarized into the following three aspects:(1) Deep integration of structural and semantic information: Breaking the barriers between structure and semantics, this approach comprehensively utilizes the network structural features of literature (co-citation) and deep semantic representations (pre-trained language models) to construct a more comprehensive and detailed topic identification method, enhancing the richness of topic expression. Simultaneously, this process achieves the mining of research foundations and frontiers, addressing the challenge of assigning multiple topics to interdisciplinary papers. (2) Achieving finegrained multi-type topic identification: By conducting semantic topic mining on citing literature through cocitation clustering, it identifies subtopics; and introduces indicators of intersectionality, burstness, and novelty to distinguish different types of subtopics, forming a hierarchical and structured topic system. (3) Revealing the evolutionary patterns of topic relationships: Tracking the evolution of subtopics and their intersectionality, burstness, and novelty indicators over time, it provides systematic and dynamic insights into the development and progression of knowledge.

[Purpose/Significance] Exploring precision acquisition strategies for electronic resources can provide an effective path for libraries to address issues such as high costs and heavy budgetary pressure caused by the traditional Big Deal procurement model. [Method/Process] This study employs Unsub to conduct an empirical analysis through the quantification of electronic resource utilization metrics (e.g., download counts, citation frequency, open access ratio) and cost-effectiveness indicators (NCPPU model). Two case studies examine the feasibility of transitioning libraries away from Big Deal subscription models. [Result/Conclusion] The study finds that the Big Deal model leads to resource waste in libraries, and Unsub provided valuable decision-making references for libraries to achieve precise resource procurement. Finally, based on the analysis results, the study proposes sustainable development recommendations tailored to the specific needs of domestic libraries for precise procurement and optimized group purchasing by library consortia.

[Purpose/Significance] Patent transfer statistics serve as an important basis for measuring the performance of university patent work and allocating policy resources. Investigations involving diverse practice actors reveal that systematic data distortion exists behind the current statistical system. Existing studies often treat transfer data as valid and focus mainly on quantitative performance, paying insufficient attention to whether such data genuinely represent technological application. Therefore, this study aims to reveal the internal logic and generation mechanism of data distortion in university patent transfer, and to provide a theoretical basis for correcting incentive orientations and promoting university patent transfer back to the essence of technological application. [Method/Process] Based on grounded theory, this study takes a key university in Shandong Province as the sampling starting point and uses snowball sampling to select 22 participants, including university faculty, enterprise executives, patent agents, and directors of science and technology offices. Semi-structured interviews were conducted to obtain original data, and multiple rounds of coding were used to analyze the relationships among influencing factors, thereby constructing a theoretical model of data distortion in university patent transfer. Sixteen transcripts were analyzed, and six were reserved for theoretical saturation testing. [Result/Conclusion] The findings show that data distortion mainly manifests as abnormal transfer purposes and disguised transfer pathways. It is driven by the interaction among flawed incentive policies, internal resistance within universities, and low patent value. Through direct causal, mediating, and constitutive relationships, these three factors form a self-reinforcing loop that generates data-distorting behaviors. Accordingly, this study recommends reconstructing the statistical logic of patent transfer to emphasize usefulness and practical implementation, reforming university incentive and management mechanisms, classifying and regulating diverse transfer pathways, and enhancing institutional inclusiveness and regulatory effectiveness. [Innovation/Value] This study makes three main contributions. First, it moves beyond the assumption that official transfer data are valid and systematically reveals the phenomenon of data distortion in university patent transfer. Second, it identifies two major manifestations—abnormal transfer purposes and disguised transfer pathways—thereby extending the analytical boundary of university patent transfer research. Third, it constructs an imbalanced triangle model of “policy flaws–internal resistance–low value,” clarifying how these three factors interact and reinforce one another to generate data distortion. Practically, the findings provide a basis for reconstructing the transfer statistics system, optimizing incentive policies, and shifting governance from “data-oriented” to “effectiveness-oriented.” [Insufficient/Improvement] The sample of this study is drawn mainly from a key comprehensive university in Shandong Province with a strong patent base, resulting in limited regional coverage and institutional diversity, which may affect the generalizability of the conclusions. Future research should expand the sample range and university types, conduct multi-case comparative analyses, and combine qualitative findings with questionnaire surveys or panel data for quantitative testing.

[Purpose/Significance] Taking the “Window of Shanghai” project as an example, this paper studies the phenomenon of knowledge flow in the book selection activities of overseas libraries in international book donation activities, and explores its knowledge flow mechanism and reproduction path. [Method/Process] Using Python for ANOVA, LSD, the K-means algorithm, and Sankey diagram plotting, the overall flow, thematic flow, and language flow of book donation selection data from 127 overseas libraries in 59 countries between 2014 and 2024 were analyzed. [Result/Conclusion] The supply mechanisms of book donation institutions interact with the contextual demands of overseas recipient libraries and the amplifying effects of institutional frameworks, collectively affecting the efficiency and depth of knowledge circulation. Designing a feedforward mechanism for book selection driven by user persona clusters facilitates dynamic responses to demand fluctuations and enables precise supply-demand matching. Furthermore, once the donated books arrive, recipient libraries can facilitate the reproduction of knowledge through curation, educational programs, and events. This research provides a reference model for public libraries to enhance the effectiveness of international book donation activities.