Artificial intelligence (AI) , as a key technology driving global digital development, has become a focal point of attention for countries around the world in the latest round of technological competition and intellectual rivalry. Interpreting the strategies of major developed countries in promoting the AI industry can provide a cutting-edge perspective and reference for our country's policy framework in the field of AI in the intelligent era. This article selects the United States, Singapore, and the United Kingdom as case studies, starting from the interpretation of AI policies and initiatives to promote AI development, focusing on the efforts of major developed countries in advancing the AI industry, and comparatively analyzing the characteristics of their AI industry strategies. The study reveals that the United States is committed to maintaining its global leadership position, encouraging technological innovation, and emphasizing ethical issues in AI; Singapore strives to become a global center for AI solutions; while the United Kingdom highlights its status as a global scientific powerhouse, focusing on building a vibrant AI ecosystem as the core of its policy.

Using the large language model ChatGPT as an example, this paper clarifies the power of truth that is unleashed by western philosophy’s activation of disruptive innovation, from the perspective of knowledge-beliefs. This offers concrete evidence supporting the proposition that the “poverty of philosophy” is the fundamental bottleneck hindering China’s technological development, thereby refuting the “uselessness of philosophy” argument. Because knowledge-beliefs are involved in innovation but are not part of innovation, “the power of the past” is difficult for observers to perceive, therefore, the process of discussing this is as follows: firstly, analyze the contents and the historical logic behind the generation of knowledge-beliefs, which are often difficult to understand; secondly, identify the function boundaries of ChatGPT that are demarcated by these knowledge-beliefs; third, provide the philosophical previsions based on the time lag between “belief and reality”; thirdly, a “Foreknowledge” methodology is proposed based on philosophical experience and knowledge organization methods. Additionally, it is argued that research on intelligence should go beyond the “reality level” and explore the “ideal level”. Furthermore, it is suggested that intelligence work needs to rediscover and enrich philosophical methods in intelligence analysis.

Traditional patent classification is carried out manually by experts. With the development of big data, artificial intelligence and natural language processing technology, automatic classification of patents is becoming one of the important research directions in both academia and industry. The text classification technology can be applied to determine whether a patent application can be granted, aiding in the automation of processing and analyzing a large number of patent documents, thereby improving work efficiency. This paper focuses on the English texts from a vast number of patents, and proposes an automatic patent text classification method based on the graph neural network model, which is used to assess whether patent applications can obtain authorization. This article utilizes the deep learning algorithm TextGCN to learn on patents’ abstracts, leveraging the neighbor information and node features of graph-structured data. Through the neural network, it generates representation vectors for patents, facilitating the forecasting of patent authorization results. The experimental findings demonstrate that the deep learning approach applied in this study achieves commendable classification results. Compared to the Doc2vec and TFIDF methods, the TextGCN model shows improvements in terms of precision, recall, accuracy, and F1 score. This method can offer a dependable research foundation for automatic prediction of the grant status of patents.

The conventional strategic-oriented competitive intelligence (CI) systems exhibit strong autonomy but fall short of meeting the intelligence demands of enterprises engaged in product development. The universally applicable product-oriented competitor analysis, lacking methodological guidance, results in the misallocation of enterprise resources due to the intricacy of information outputs and the deficiency of fostering innovation. Hence, the imperative lies in crafting a product-relevance intelligence system guided by internet-derived information. Upon the established methodology for crafting key intelligence topics (KITs), we introduce the concept of product-oriented key intelligence topics (PKITs), delineate the four distinct domains of PKITs, and outline the methods and procedures for system aggregation. The collection of market, user, and technical information is a prerequisite for the intelligence system of enterprise product development. In dynamic market environments, it is necessary to extend forward-looking intelligence thinking and develop agile intelligence processes to improve the competitiveness of enterprise products.

As Japan’s industrial technology innovation accelerator, new energy industrial technology development organization gathers officials, industries and universities, with the main task of “strengthening industrial technology” and “solving energy and earth environmental problems”, and is committed to exploring the seeds of future industrial core technologies, strengthening the foundation of medium and long-term industrial competitiveness, and the practical application of research results. On the basis of introducing the background information of the institution, such as its history, function positioning, research and development fields, organizational structure and operation, this paper attempts to sort out and analyze the characteristic operation mode and cases of the institution. Based on this, the successful experience of the institute is summarized from the aspects of scientific research management, government-industry-academic coordination, international cooperation, etc., which provides reference and reference for China to further improve its own science and technology innovation system and realize industrial upgrading and transformation and sustainable development.

Integrating resource transmission, collaborative filtering, and link prediction theories to systematically and innovatively express the “enterprise patent” relationship, and then accurately and effectively identify potential cooperative relationships between enterprises, helps enterprises enrich their technological dimensions and promote the process of industrial innovation. Based on competitive intelligence thinking and data from 7 415 technical patents in the field of perovskite solar cells, a dynamic transfer recommendation based on patent resource transmission, a heterogeneous network recommendation based on link prediction method, and a collaborative filtering recommendation based on patent text are constructed. By introducing adjustable parameters, the three are integrated into a recommendation system, enable enterprises to select patent resources and partners that require attention based on real-life scenarios. Research shows that the fusion perspective of hybrid recommendation systems provides a new research method for predicting potential relationships in disruptive technology enterprises. The constructed recommendation system achieves multi-dimensional expression of recommendation results that are “accurate, novel, and comprehensive”, providing decision-making and reference for future cooperation between enterprises.

Through comparative analysis of the evolution and hot topics of open science research in the field of library and information science at home and abroad, it provides reference for exploring the development of open science research in the field of library and information science in China. With Chinese and foreign literature as a sample, visual analysis, clustering analysis on the hot topics at home and abroad, distribution trend of research forces at home and abroad, high-impact author, statistics of research institutions, keyword statistics are conducted. The results finds the national distribution of open science research in the field of library and information science at home and abroad, the time distribution of major authors, major research institutions and key words, and the changes in contribution countries to the publication of papers, reveal the differences between the main research

New quality productivity is the core engine for enhancing international competitiveness and securing future growth. This paper references internationally recognized rankings and combines industry trends to conduct a comparative analysis of the development of new quality productivity in China and the United States from three dimensions: innovation, creativity, and transformative power. The study shows that the United States holds significant advantages in its technological innovation system and industrial ecosystem, particularly in cuttingedge technologies. Through high-intensity R&D investment and the rise of unicorn companies, the U.S. has established a leading global industrial layout. In contrast, China demonstrates potential in catching up in certain
advanced manufacturing sectors, but still lags behind in achieving major innovative breakthroughs and in the completeness of its innovation ecosystem.

This article fully utilizes the information of patent applications and rights holders contained in patent literature, and constructs an effective method for identifying technological opportunities from the perspective of patent applicants/patent holders, providing decision-making support for innovative entities to carry out technological innovation activities. Firstly, using invention applications and authorized inventions as data sources and based on the LDA model to explore potential technical topics, by cross judging the attention of patent applicants from enterprises and research institutes in recent years, identify technology opportunities with great development potential, and classify them into subject focused, market transformation, and R&D potential types. Secondly, using valid patents as the data source, by introducing patent strength indicators based on patent portfolio analysis methods, the primary driving entities of various types of technological opportunities are identified, and the technical competition elements of representative primary driving entities are analyzed in detail. In the empirical part, taking intelligent connected vehicles as an example, a total of 9 technological opportunities were identified, including 3 main focus, 3 market transformation, and 3 research and development potential each, with enterprises as the primary driving force.The results indicate that based on the cross trend of the number of invention applications by the “patent applicant/patentee” subject and the patent intensity of the two types of subjects, different types of technological opportunities with great development potential can be identified, and their market competition intensity can be judged, providing a new perspective for identifying technological opportunities.

With the development of the industrial internet, the manufacturing industry is gradually transitioning from traditional product manufacturing models to emerging service-oriented models. This article selects 60 manufacturing enterprises from the panel of A-share listed manufacturing enterprises in Shanghai and Shenzhen as the research objects, and uses the fuzzy set qualitative comparative analysis (fsQCA) method to deeply explore the service-oriented transformation paths under different conditions from the perspective of “R&D manufacturing supply chain” in the manufacturing value chain. Research has found that: the level of digitalization is a  necessary condition for the service-oriented transformation of manufacturing enterprises; The service-oriented

近年来，我国科技情报界对于情报支持科学决策和战略决策的功能与作用的认识与实践在不断提升与深化。相对而言，对于情报支撑服务科技创新的思考研究与实践探索是不充分的。前者是“对上的服务”，为政产学研的中高层决策者服务，这基本属于“智库建设”范畴，这里暂且称之为情报决策服务；后者是“对下的服务”，直面科学研究、技术创新的一线与主体，为科学家和工程师服务，这里暂且称其为情报专业服务。在信息化的进程中，数字化一直在赋能图情行业，不断尝试提升支撑科技创新的情报服务的效率和效能，但当下的智慧化正在颠覆情报专业服务。

As a “star player” in biological manufacturing, synthetic biology is reshaping many industries at an unprecedented rate, including biopharmaceutical, biological agriculture, personal care, high-performance materials, chemical energy and food health. Through further analysis of various links of synthetic biology industry chain, including upstream enabling technology, midstream enabling platform and downstream innovative application, we can find that this emerging field has vigorous vitality and great potential; by looking into the future development trend of this field, we can discover new industrial track, guide the future development of life and health, and stimulate the potential of new quality productivity.

Artificial intelligence (AI) technology, with its powerful data processing capabilities and deep learning algorithms, is driving unprecedented changes in the pharmaceutical industry. These changes span from the initial discovery and refined design of drugs, to the accelerated launch and intelligent production of pharmaceuticals, and even to the precision marketing of strategies and personalized medical services. Through the literature search method, this article systematically explores AI’s impact on various aspects of the pharmaceutical industry, including drug discovery and design, clinical trials, product launch and production management, marketing strategies. It highlights how AI has driven profound changes in the industry and summarizes the challenges faced by AI technology in the pharmaceutical field, providing a forward-looking outlook on future development trends. The global market value of AI in drug discovery is expected to continue growing, and its role in biopharmaceutical research and manufacturing is gradually emerging. AI is anticipated to bring sustained momentum to the development of the pharmaceutical industry.

The prediction of emerging technologies based on patent text data is of great significance for managers, who must focus on the direction of technology development, adjust technology research and development routes, and take the initiative in technology competitions. Taking the AI technology as an example, firstly, use grammatical analysis technology to identify noun phrases in English sentences and construct a noun phrase co-occurrence graph through the network word co-occurrence method. Secondly, a graph neural network model is built to implement co-occurrence graph link prediction and regression. Finally, link prediction and link regression results are combined to make technical predictions for artificial intelligence technology. The experimental results show that using the graph neural network to fuse the noun phrase co-occurrence graph method is more suitable for the prediction of emerging technologies in complex semantic situations and can obtain smaller prediction recognition granularity; in addition, the prediction results show that artificial intelligence technology is forward to application development in electronic meetings, computer vision, medical health, interactive interfaces, measurement and monitoring, machine learning algorithms, sensors, data channels, and intelligent manufacturing.