Library subject service intelligent recommendation method and system based on big data mining

Abstract

The invention provides a library subject service intelligent recommendation method and system based on big data mining, and relates to the technical field of library services.The method comprises the steps that firstly, library subject service big data is mined, and subject user research process features covering subject user research stages, resource use tracks and demand evolution features are constructed; and establishing a dynamic adaptation relationship between the research process and the resources in combination with a library subject resource pool. And splitting resource service modules according to research process stages to form staged subject resource service architectures, and generating a staged subject service recommendation list after series connection. And collecting user research process data in real time, dynamically adjusting a recommendation list, supplementing transition resources and services, and forming dynamic subject service recommendation contents. And finally, outputting recommended content and collecting feedback data, and integrating big data to optimize research process characteristics and dynamic adaptation relationships. According to the invention, accurate, dynamic and systematic library subject service recommendation can be provided for the user, and the service quality and the user research efficiency are improved.

Description

Technical Field

[0001] This application relates to the field of library service technology, and more specifically, to a method and system for intelligent recommendation of library subject services based on big data mining. Background Technology

[0002] In the field of library services, with the continuous enrichment of subject knowledge and the increasing diversification of user research needs, traditional library subject service models have gradually revealed many limitations. Currently, most libraries, when providing subject services, primarily recommend resources to users based on general classification systems. This recommendation method lacks a precise grasp of the research progress of subject users. Subject users exhibit significant differences in resource needs and focus at different research stages, and traditional methods fail to fully consider this dynamic change, resulting in a low degree of matching between recommended resources and users' actual needs.

[0003] Meanwhile, existing library subject services are fragmented in terms of resource integration and service delivery, lacking a systematic, phased service architecture. During the research process, users often need to manually filter and integrate different resources and services, which not only increases their time costs but may also negatively impact research efficiency due to information overload. Furthermore, existing services lack a mechanism for real-time data collection and dynamic adjustment of user research progress, failing to optimize recommended content in a timely manner based on changes in the user's research progress, and thus failing to meet users' growing demands for personalized and dynamic services. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of this application, embodiments of this application provide a method for intelligent recommendation of library subject services based on big data mining, the method comprising: By mining big data on library subject services, constructing the research process characteristics of subject users, retrieving the library's subject resource pool, and establishing a dynamic adaptation relationship between research process and resources in combination with the library's subject resource pool, the research process characteristics of subject users cover the research stages of subject users, resource usage trajectory, and demand evolution characteristics. Based on the dynamic adaptation relationship, the resource service modules are divided according to the research process stages of the user discipline. Each resource service module corresponds to the core resource needs and supporting services of a research stage, forming a phased discipline resource service architecture. By connecting the aforementioned phased subject resource service architecture and integrating resource service modules according to the research progress logic, a phased subject service recommendation list is generated, which carries the resource details and connection guidelines for each phase. Real-time collection of research progress data from subject users, dynamic adjustment of the phased subject service recommendation list, optimization of module connection logic, supplementation of transitional resources and services during research progress transition periods, and formation of dynamic subject service recommendation content; Output the recommended content of the dynamic subject service, collect process adaptation feedback data, integrate the process adaptation feedback data into the library subject service big data, and optimize the research process characteristics of the subject users and the dynamic adaptation relationship based on the updated library subject service big data.

[0005] In another aspect, embodiments of this application also provide an intelligent recommendation system for library subject services based on big data mining, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the machine-readable storage medium to implement the above-described method.

[0006] Based on the above, this invention mines big data from library subject services to construct research process characteristics of subject users. These characteristics encompass the research stages, resource usage trajectories, and evolving needs of subject users. A dynamic adaptation relationship is established based on this, accurately reflecting the intrinsic connection between research progress and resources. Resource service modules are broken down according to research process stages to form a phased subject resource service architecture, achieving systematization and modularization of resource services. The phased subject resource service recommendation list generated by the phased architecture not only contains resource details for each stage but also provides transition guidance, facilitating a smooth transition between different research stages for users. Real-time collection of subject user research progress data and dynamic adjustment of the recommendation list can promptly respond to changes in user research progress, optimize module transition logic, and supplement transitional resources and services, ensuring the dynamism and adaptability of the recommended content. Integrating process adaptation feedback data into big data and optimizing relevant characteristics and adaptation relationships continuously improves the accuracy of recommendations and service quality, providing users with a more personalized, efficient, and high-quality library subject service experience. Attached Figure Description

[0007] Figure 1 This is a schematic diagram illustrating the execution flow of the intelligent recommendation method for library subject services based on big data mining provided in this application embodiment.

[0008] Figure 2 This is a schematic diagram of the hardware architecture of the intelligent recommendation system for library subject services based on big data mining provided in this application embodiment. Detailed Implementation

[0009] The present application will now be described in detail with reference to the accompanying drawings. Figure 1This is a flowchart illustrating an embodiment of the intelligent recommendation method for library subject services based on big data mining provided in this application. The following is a detailed description of this intelligent recommendation method for library subject services based on big data mining.

[0010] Step S110: Mine the big data of library subject services, construct the research process characteristics of subject users, retrieve the library subject resource pool, and establish a dynamic adaptation relationship between research process and resources in combination with the library subject resource pool. The research process characteristics of subject users cover the research stage of subject users, resource usage trajectory and demand evolution characteristics.

[0011] This embodiment focuses on graduate students in the field of computer science and technology. First, it mines big data from library subject services, using data cleaning and feature extraction to construct research progress characteristics of subject users from data such as resource borrowing, document downloading, and consultation interactions. Simultaneously, it retrieves the library's subject resource pool, which includes various resource types such as academic literature, software tools, and experimental data. Correlation analysis is then performed between the research progress characteristics of subject users and the resource pool to establish a dynamic adaptation relationship between different stages of the research process and various resources, ensuring that resource recommendations match the changing needs of users at different research stages.

[0012] Step S111: Retrieve the library's subject service big data, extract basic information of subject users, resource borrowing records, document download trajectory, consultation interaction content, research project identifiers and output records from the library's subject service big data, remove invalid data without research focus, extract the subject user research stage characteristics from the extracted valid data, the subject user research stage characteristics include topic selection and research stage, theoretical research stage, experimental verification stage, result writing stage and revision and improvement stage, and mark the time node and resource usage characteristics of each stage.

[0013] The library database retrieved subject-specific service big data in the field of computer science and technology, extracting basic user information, including major (e.g., artificial intelligence, data science), year of study, etc.; resource borrowing records, covering borrowed paper documents; document download history, recording the download status of electronic documents; consultation interaction content, including user inquiries and responses; research project identifiers, used to identify projects participated in by users; and output records, such as published papers and patent applications. The extracted data was filtered to remove invalid data, such as borrowing records of leisure books. From the valid data, the research stage characteristics of subject users were extracted, clearly dividing the data into five stages: topic selection research, theoretical research, experimental verification, and result writing and revision. Time nodes for each stage were marked; for example, the topic selection research stage begins in the first semester after enrollment and lasts approximately eight weeks. Resource usage characteristics for each stage were also recorded, such as the high frequency of use of review articles during the topic selection research stage.

[0014] Step S112: Connect the resource usage trajectories of each research stage, analyze the switching patterns of resource types, changes in usage frequency, and evolution of consultation questions in the resource usage trajectories, and summarize the evolution characteristics of user needs in the discipline by combining the marked stage time nodes and resource usage characteristics, and form a demand evolution path.

[0015] By sequentially connecting the resource usage trajectories of users in the field of computer science and technology at each research stage, a complete trajectory sequence is formed. The switching patterns of resource types within this sequence are analyzed, such as the shift from review articles in the topic selection and research stage to core journal articles in the theoretical research stage; changes in usage frequency, such as the increased frequency of use of specific theoretical literature in the theoretical research stage; and the evolution of consultation questions, from topic-related questions to theoretical discussion questions. Combining the time nodes of each stage and the characteristics of resource usage, the evolutionary characteristics of user needs are summarized, forming a demand evolution path. This path demonstrates the process of user needs changing from vague to clear, and from basic to in-depth.

[0016] Step S1121: Extract resource usage trajectory data for each research stage from the valid data after stripping away invalid data, and string them together in chronological order to form a complete trajectory sequence. Each trajectory point corresponds to one resource usage behavior and associated information.

[0017] From the valid data after stripping away invalid data, resource usage trajectory data of users in the field of computer science and technology at various research stages is extracted. This resource usage trajectory data includes specific information about each resource usage behavior, such as resource name, resource type, usage time, and usage duration. These resource usage behaviors are then linked together chronologically to form a complete trajectory sequence. Each trajectory point corresponds to a specific resource usage behavior and related information, such as the user's research stage and the question asked at the time of the behavior.

[0018] Step S1122: Analyze the resource type switching in the complete trajectory sequence, statistically analyze the usage ratio and switching frequency of different types of resources in each research stage, summarize the type switching pattern, and determine the type evolution trend of resource demand.

[0019] This study analyzes the switching patterns of resource types throughout the complete research trajectory, statistically analyzing the usage percentage of different resource types in the field of computer science and technology at each research stage. For example, in the topic selection and research stage, the usage percentage of review articles may reach 40%, while cutting-edge research reports may account for 30%. It also analyzes the frequency of resource type switching, such as the frequency of switching from review articles to core journal papers. Based on these statistics, the study summarizes the patterns of resource type switching, thereby determining the evolution trend of resource demand types. For instance, as the research progresses, resource demand gradually shifts from review-based resources to specialized and practical resources.

[0020] Step S1123: Based on the statistically obtained resource usage ratio and switching frequency, calculate the usage frequency change curve of resource usage frequency in each research stage, extract the time nodes, resource types and consultation content corresponding to the frequency peaks and troughs, and conduct correlation analysis on the relationship between frequency changes and demand.

[0021] Based on statistically obtained resource usage percentages and switching frequencies, we calculated the change curves of resource usage frequency in each research stage within the field of computer science and technology. Analysis of these curves revealed the time points corresponding to frequency peaks and troughs, such as a frequency peak occurring in the fourth week of the theoretical research stage; the corresponding resource types, such as literature on a specific theoretical model; and the content of user inquiries during that time period. Correlation analysis of this information explored the relationship between frequency changes and user needs, for example, whether frequency peaks corresponded to users' concentrated learning needs for a particular theoretical knowledge point.

[0022] Step S1124: Organize the consultation questions from each research stage from the valid data, classify them according to the nature of the questions to form a consultation question sequence, and analyze the evolution of the questions from basic types to in-depth types, and from resource acquisition types to outcome optimization types.

[0023] We compiled consultation questions from users in the computer science and technology field at various research stages from valid data, categorizing them by question nature, such as resource acquisition, theoretical understanding, experimental techniques, and results optimization, forming a consultation question sequence. We analyzed the evolution of question types within this sequence, observing how questions gradually shifted from basic resource acquisition types to in-depth theoretical understanding and results optimization types, such as from "how to find literature in a certain field" to "optimization methods for a certain algorithm."

[0024] Step S1125: Combining the type switching pattern, the usage frequency change curve, and the consultation question sequence, extract the co-evolutionary characteristics among the type switching pattern, the usage frequency change curve, and the consultation question sequence to determine the core orientation and driving force of the needs at different research stages.

[0025] This study combines the switching patterns of resource types in the field of computer science and technology, the frequency of use curves, and the sequence of consultation questions to analyze their synergistic evolution characteristics. For example, when the resource type switches to experimental methodologies, the frequency of use curve shows a peak, and the consultation questions are mostly related to experimental techniques, indicating a synergistic relationship among these three factors. Through this synergistic analysis, the core demands of different research stages are identified. For instance, the core demands of the experimental verification stage are experimental methods and data processing, as well as the driving forces behind changes in demands, such as the progress of research and the technical challenges encountered.

[0026] Step S1126: Based on the characteristics of co-evolution and the core orientation of demand, summarize the demand evolution nodes of discipline users in the research process. The demand evolution nodes include demand upgrade nodes, demand transformation nodes and demand expansion nodes, and mark the resource service characteristics corresponding to each node.

[0027] Based on the characteristics of co-evolution and the core orientation of needs, this paper summarizes the nodes of demand evolution for users in the field of computer science and technology during their research process. These nodes include demand upgrade nodes, which are the increase in the depth or breadth of users' needs for existing resources or services; demand transformation nodes, which are the fundamental changes in the type of user needs, such as from theoretical research needs to experimental verification needs; and demand expansion nodes, which are the emergence of new, previously unseen types of user needs. Each demand evolution node is labeled with corresponding resource and service characteristics; for example, demand upgrade nodes correspond to higher-level literature resources, and demand transformation nodes correspond to experimental-related services.

[0028] Step S1127: Integrate the nodes, trends and driving forces of demand evolution, and form a framework for demand evolution path according to the research stage sequence. Each stage corresponds to an evolution process and characteristic description.

[0029] This paper integrates the evolution nodes, trends, and driving forces of user needs in the fields of computer science and technology, and forms a framework for the evolution path of needs according to the research stages: topic selection and research, theoretical research, experimental verification, results writing, and revision and improvement. Within this framework, each research stage corresponds to a specific evolution process and characteristic description of needs. For example, the evolution of needs in the theoretical research stage progresses from the need to understand basic theories to the need to apply complex theoretical models, characterized by an increase in the frequency of use in core journal articles.

[0030] Step S1128: Based on the demand evolution path framework, supplement the adaptation suggestions and determine the resource service adjustment direction corresponding to each evolution node.

[0031] Based on the framework of demand evolution paths, adaptation suggestions are added to determine the corresponding direction of resource service adjustments for each demand evolution node of users in the fields of computer science and technology. For example, when users reach the demand upgrade node, the adaptation suggestion is to increase the recommendation of high-impact journal papers; when users are in the demand transformation node, the adaptation suggestion is to provide experimental design-related services and resources.

[0032] Step S1129: Arrange the contents in the demand evolution path framework according to time sequence and evolution logic, mark the relationship between each element, generate the demand evolution path, output the demand evolution path and corresponding adaptation suggestions, and store them synchronously in the subject service data center.

[0033] The content within the demand evolution path framework is arranged chronologically and logically, clarifying the relationships between various elements, such as the correlation between demand evolution nodes and resource type switching, and the correlation between the driving forces of demand changes and consulting issues. A complete demand evolution path is generated, and the path and corresponding adaptation suggestions are output and synchronously stored in the subject service data center.

[0034] Step S113: Integrate the research stage characteristics, resource usage trajectory, and demand evolution path, classify them by subject area and research direction, and construct initial subject user research process characteristics. Each initial subject user research process characteristic corresponds to a subject user research mode.

[0035] This study integrates the research stage characteristics, resource usage trajectories, and demand evolution paths of users in the fields of computer science and technology, categorizing them according to subject areas such as computer science and technology, and more specific research directions such as machine learning in artificial intelligence and data mining in data science. Based on these different categories, initial research process characteristics of users in each subject area are constructed. Each initial characteristic corresponds to a research pattern of users in a subject area with similar research behaviors and needs. For example, users in the machine learning research direction may have specific resource usage trajectories and demand evolution paths.

[0036] Step S114: Retrieve the library's subject resource pool, extract the subject attributes, applicable research stages, content focus, and supporting service descriptions of the resources in the library's subject resource pool, and establish resource stage adaptation tags, with each resource corresponding to at least one research stage tag.

[0037] Retrieve the library's subject-specific resource pool from the library's resource management system, extracting detailed information about the resources within the pool. This includes subject attributes, such as which research direction within the field of computer science and technology it belongs to; applicable research stage, such as whether the resource is suitable for the topic selection and research stage or the experimental verification stage; content focus, such as whether the resource emphasizes theoretical introduction or experimental methods; and supporting service descriptions, such as whether the resource provides document delivery, interlibrary loan, or other supporting services. Based on this information, create a resource stage-appropriate tag for each resource, ensuring that each resource corresponds to at least one research stage tag, such as "Topic Selection and Research Stage - Review Literature".

[0038] Step S115: Associate and match the initial subject user research process characteristics with the resource stage adaptation tags, extract the resource tags corresponding to each research stage, label them as process-related resource tags, and establish a dynamic adaptation relationship between research process and resources based on the usage frequency and demand matching degree of the process-related resource tags in each research stage, and determine the core resources and extended resources of each research stage.

[0039] The research process characteristics of initial subject users are correlated and matched with resource stage adaptation tags. For each research stage in the field of computer science and technology, corresponding resource tags are extracted and labeled as process-related resource tags. Based on the frequency of use of these process-related resource tags in each research stage (e.g., the high frequency of use of a certain type of core journal articles in the theoretical research stage) and the degree of demand matching (e.g., the degree of fit between resource content and the needs of that stage), a dynamic adaptation relationship between research process and resources is established. Through this relationship, the core resources of each research stage are identified, namely resources with high usage frequency and high demand matching, as well as extended resources, namely resources that complement the core resources.

[0040] Step S116: Supplement the service adaptation content in the dynamic adaptation relationship, determine the corresponding service type, service process and connection time for each research stage, after supplementing the adaptation relationship dimension, integrate the dynamic adaptation relationship into the initial discipline user research process characteristics, supplement the adaptation rules and demand evolution adaptation logic, form the discipline user research process characteristics, and output the discipline user research process characteristics and the dynamic adaptation relationship, and synchronously store them in the discipline service data center.

[0041] The dynamic adaptation relationship is supplemented with service adaptation content. For each research stage in the field of computer science and technology, corresponding supporting service types are determined, such as topic selection consultation services during the topic research stage and document delivery services during the theoretical research stage. Service processes are clarified, such as the steps required for document delivery services: user application submission, librarian review, resource acquisition, and delivery to the user. Timing of integration is determined, such as providing guidance on using document management tools at the initial stage of theoretical research. After supplementing the adaptation relationship dimension, the dynamic adaptation relationship is integrated into the initial disciplinary user research process characteristics, supplementing adaptation rules, such as recommending extended resources after users use core resources a certain frequency, and the logic of demand evolution adaptation, such as adjusting resource service recommendations based on the user's demand evolution path. The final disciplinary user research process characteristics are formed, and these characteristics and the dynamic adaptation relationship are output and synchronously stored in the disciplinary service data center.

[0042] Step S120: Based on the dynamic adaptation relationship, the resource service modules are split according to the research process stages of the discipline users. Each resource service module corresponds to the core resource needs and supporting services of a research stage, forming a phased discipline resource service architecture.

[0043] Based on the established dynamic adaptation relationship, and according to the five stages of the research process for users in the field of computer science and technology—namely, topic selection and research, theoretical research, experimental verification, and writing and revising results—resource service modules are divided. Each resource service module corresponds to the core resource needs and supporting services of a research stage. For example, the module for the topic selection and research stage includes core resources such as review articles and supporting services such as topic selection consultation. Organizing these modules in the order of stages forms a phased subject resource service architecture, which demonstrates the combination of resource services for different research stages.

[0044] Step S121: Retrieve the research process characteristics of the subject users and the dynamic adaptation relationship from the subject service data center, and divide the resource service modules into the topic selection and research stage, theoretical research stage, experimental verification stage, result writing stage, and modification and improvement stage. Each resource service module corresponds to a research stage, and the module name and core service objectives of each resource service module are marked.

[0045] The system retrieves the research progress characteristics and dynamic adaptation relationships of users in the field of computer science and technology from the subject service data center. Based on the five stages of the research process—topic selection and research, theoretical research, experimental verification, results writing, and revision and improvement—corresponding resource service modules are extracted. Each module corresponds to a research stage, and each module is labeled with a name, such as "Resource Service Module for Topic Selection and Research Stage," and its core service objectives are clearly defined. For example, the core service objective of the Topic Selection and Research Stage module is to help users quickly determine their research direction and obtain cutting-edge information in related fields.

[0046] Step S122: For the topic selection and research phase module, retrieve the core resources corresponding to the topic selection and research phase in the dynamic adaptation relationship. The core resources include subject review literature, cutting-edge research reports, topic selection reference materials and relevant policy documents, and extract basic information and acquisition channels of the resources.

[0047] For the topic selection and research phase, core resources corresponding to this phase are retrieved from the dynamic adaptation relationship. These core resources include review literature in the field of computer science and technology, covering the current research status, hot topics, and trends in the field; cutting-edge research reports, such as the latest technology development reports and industry research reports; topic selection reference materials, such as past excellent topic selection cases and supervisor-recommended topic directions; and relevant policy documents, such as national science and technology development plans and research project application guidelines. Basic information of these core resources is extracted, such as resource name, author, publishing institution, publication date, and access channels, such as library location, electronic resource database links, and interlibrary loan methods.

[0048] Step S123: Based on the extracted core resource information and acquisition channels of the topic selection and research stage, supplement the supporting services of the topic selection and research stage module. The supporting services include topic selection consultation services, literature retrieval training, resource navigation services and research direction analysis services. Determine the service content and appointment method.

[0049] Based on the fundamental information and acquisition channels of core resources in the topic selection and research phase, supplementary services are provided to this module. These supplementary services include: topic selection consultation services, where subject librarians provide one-on-one guidance on topic selection, analyzing the feasibility and innovativeness of the proposed topic; literature retrieval training, teaching users how to efficiently use the library's search tools to obtain the necessary literature resources; resource navigation services, guiding users to quickly find various resources related to their topic; and research direction analysis services, analyzing potential research directions for users based on the dynamics of discipline development and user interests. The specific content of each service is determined, such as the topic selection consultation service including outlining topic selection ideas and providing suggestions on literature research methods, and the appointment methods are specified, such as online appointments through the library's website, telephone appointments, and email appointments.

[0050] Step S124: For the theoretical research stage module, retrieve the core resources corresponding to the theoretical research stage in the dynamic adaptation relationship, integrate the core resources into discipline monographs, core journal articles, theoretical model literature and interdisciplinary theoretical materials, and supplement the theoretical depth explanation and citation suggestions of the resources.

[0051] For the theoretical research phase module, core resources corresponding to this phase are retrieved from the dynamic adaptation relationship and integrated into: academic monographs in the field of computer science and technology, systematically introducing the fundamental theories and important concepts of the field; core journal articles, showcasing the latest research results published in top journals in the field; theoretical model literature, detailing the construction methods and application scenarios of various theoretical models; and interdisciplinary theoretical materials, involving theoretical knowledge from other disciplines intersecting with computer science and technology. The integrated core resources are supplemented with explanations of their theoretical depth, such as whether a particular monograph is at an introductory, intermediate, or expert level, as well as citation suggestions, such as how to properly cite these resources when writing papers.

[0052] Step S125: Based on the integrated core resources and supplementary instructions for the theoretical research stage, configure the supporting services for the theoretical research stage module. The supporting services include document delivery services, interlibrary loan services, consultation services for theoretical difficulties, and guidance on the use of document management tools, as well as supplementary service process details.

[0053] Based on the integrated core resources and supplementary explanations for the theoretical research phase, supporting services for this phase module are configured. These services include: document delivery service (obtaining and delivering electronic documents to users through cooperation with other libraries when the library does not have the required electronic documents); interlibrary loan service (allowing users to borrow print resources not available in the library); theoretical difficulty consultation service (subject librarians or experts in related fields answering questions encountered by users during theoretical learning); and guidance on using document management tools (teaching users how to manage and cite references using tools such as EndNote and Zotero). Supplementary service process details are provided, such as the document delivery service process: user submits application, librarian reviews, sends request to partner libraries, receives documents, and forwards to the user, clearly defining the operational requirements and time limits for each step.

[0054] Step S126: For the experimental verification stage, the results writing stage, and the modification and improvement stage modules, according to the configuration logic of the topic selection and research stage and the theoretical research stage modules, retrieve the core resources and extended resources of the corresponding stages in the dynamic adaptation relationship, supplement the supporting services of each stage, and make each resource service module form a resource service combination.

[0055] The three modules—experimental verification, results writing, and revision and improvement—are processed according to the configuration logic of the topic selection and research and theoretical research modules. That is, for each stage, corresponding core resources and extended resources are retrieved from the dynamic adaptation relationship to supplement supporting services, so that each module forms a complete resource service combination.

[0056] Step S1261: For the experimental verification stage module, retrieve the core resources corresponding to the experimental verification stage in the dynamic adaptation relationship. The core resources include experimental method literature, data manuals, instrument operation guides, experimental design cases and similar experimental research results, and extract basic resource information and usage scenario descriptions.

[0057] For the experimental verification phase module, core resources corresponding to this phase are retrieved from the dynamic adaptation relationship. These core resources include experimental methodology literature in the field of computer science and technology, introducing various experimental design and implementation methods; data manuals, containing experimental-related data standards, parameter ranges, etc.; instrument operation guides, detailing the operation steps and precautions of experimental instruments; experimental design cases, showcasing excellent experimental design examples; and similar experimental research results, the achievements of other researchers under similar experimental conditions. Basic information of these core resources is extracted, such as resource name, author, publishing institution, and usage scenario descriptions, such as whether a certain experimental methodology literature is suitable for image recognition experiments, or whether a certain instrument operation guide is for a specific model of computer equipment.

[0058] Step S1262: Based on the extracted basic information and usage scenario descriptions of the core resources in the experimental verification stage, supplement the extended resources of the experimental verification stage module. The extended resources include experimental data processing tools, statistical analysis literature, experimental error analysis data and alternative experimental schemes, and mark the compatibility relationship between the resources and the core resources.

[0059] Based on the fundamental information and usage scenarios of the core resources in the experimental verification phase, this module supplements its extended resources. These extended resources include experimental data processing tools, such as MATLAB and Python data processing libraries; statistical analysis literature introducing statistical analysis methods for experimental data; experimental error analysis materials explaining how to identify and analyze experimental errors; and alternative experimental schemes, providing alternative solutions when the original experimental scheme is not feasible. The compatibility between the extended resources and the core resources is indicated; for example, a certain experimental data processing tool is compatible with specific experimental method literature for processing the data generated by that experiment.

[0060] Step S1263: Based on the core resources of the experimental verification stage after supplementing and extending the resources, configure the supporting services for the experimental verification stage module. The supporting services include experimental resource reservation service, data processing consultation, experimental scheme optimization guidance and experimental result storage service, and determine the service process and delivery standards.

[0061] Based on the core resources of the experimental verification phase after supplementing and extending resources, the supporting services for this module are configured. These supporting services include: experimental resource reservation service, helping users reserve the necessary instruments, equipment, and software tools for experiments; data processing consultation, with professionals providing consultation and guidance on experimental data processing methods; experimental scheme optimization guidance, evaluating and providing optimization suggestions for users' experimental schemes; and experimental result storage service, providing users with secure and reliable storage space for experimental data and results. The processes for each service are defined, such as the experimental resource reservation service process involving submitting a reservation request, checking resource availability, confirming the reservation, and receiving usage reminders; and delivery standards are clearly defined, such as requiring a response time of 24 hours for data processing consultation, and providing detailed processing method documentation.

[0062] Step S1264: For the results writing stage module, retrieve the core resources corresponding to the results writing stage in the dynamic adaptation relationship, integrate the core resources into paper writing specifications, reference format guidelines, journal directory for results publication, literature on writing skills and similar results examples, and supplement the applicable scenarios and usage suggestions of the resources.

[0063] For the research output writing phase, core resources corresponding to this phase are retrieved from a dynamically adapted database and integrated into a set of writing guidelines for computer science and technology papers, specifying requirements for structure, format, and language expression; a reference format guide, detailing the citation formats for different types of documents; a directory of journals for publication, listing important journals in the field and their submission requirements; writing skills literature, introducing practical techniques and methods for paper writing; and sample papers of similar research outputs, showcasing excellent examples. To supplement the integrated core resources, applicable scenarios are added, such as whether a writing skill is applicable to the writing of the introduction section of a paper, and usage suggestions, such as avoiding plagiarism when referring to sample papers of similar research outputs.

[0064] Step S1265: Based on the integrated core resources and supplementary suggestions for the result writing stage, add extended resources for the result writing stage module. The extended resources include instructions for using plagiarism detection tools, polishing service guidelines, copyright knowledge materials, and publication process analysis, supplementing resource service dimensions.

[0065] Based on the integrated core resources and supplementary suggestions for the writing stage, extended resources have been added to this module. These extended resources include instructions on using plagiarism detection tools such as CNKI and Turnitin; a polishing service guide introducing the service content, process, and pricing standards for paper polishing; copyright information explaining copyright issues and precautions during the paper publication process; and a publication process analysis detailing the entire process from submission to acceptance and publication. By adding these extended resources, the service dimensions are expanded, providing users with more comprehensive support for writing their work.

[0066] Step S1266: Based on the core resources of the result writing stage after adding extended resources, configure the supporting services of the result writing stage module. The supporting services include paper polishing service, plagiarism check service, publication consultation service, reference organization service and result archiving guidance, and determine the service content and related specifications.

[0067] Based on the core resources for the writing stage after adding extended resources, the supporting services for this module are configured. These services include: paper polishing services, which modify and optimize the language, logical structure, and formatting of the paper; plagiarism checking services, which help users detect the similarity rate of their papers and provide suggestions for revision; publication consultation services, which provide users with advice on journal selection, submission strategies, and responses to peer review comments; reference organization services, which assist users in properly organizing the references in their papers; and results archiving guidance, which guides users in archiving their results according to the requirements of their school or institution. The specific content of each service is determined, such as the paper polishing service including grammatical error correction and improvement of fluency, and relevant standards are clarified, such as ensuring the accuracy of plagiarism detection results and protecting the user's intellectual property rights in the paper polishing service.

[0068] Step S1267: For the modification and improvement stage module, retrieve the core resources corresponding to the modification and improvement stage in the dynamic adaptation relationship. The core resources include the peer review comment response guidelines, modification case analysis, academic norm supplementary materials, and result optimization literature, and extract the core value and application methods of the resources.

[0069] For the revision and improvement phase module, core resources corresponding to this phase are retrieved from the dynamic adaptation relationship. These core resources include: a guide to responding to reviewers' comments, instructing users on how to correctly understand and respond to reviewers' comments; revision case studies, demonstrating the thought process and methods for revising papers through practical examples; supplementary materials on academic norms, supplementing details on academic norms that need to be paid attention to during the paper revision process; and optimized literature, introducing how to improve the academic quality and impact of the paper. The core value of these core resources is extracted; for example, the core value of the reviewer comment response guide is to help users improve the efficiency and quality of their responses, as well as application methods, such as how to combine reviewers' comments and revision case studies to revise the paper.

[0070] Step S1268: Based on the core value and application methods of the extracted core resources in the modification and improvement stage, supplement the extended resources of the modification and improvement stage module. The extended resources include peer review comments, modification tool recommendations, result display templates and subsequent research extension materials, and mark the compatibility relationship between the resources and the core resources.

[0071] Based on the core value and application methods of the core resources in the revision and improvement phase, this module is supplemented with extended resources. These extended resources include a collection of peer review comments, gathering peer review opinions from multiple papers in the field for user reference; recommendations for editing tools, such as grammar checkers and formatting tools; templates for showcasing results, such as academic conference PPT templates and research report templates; and supplementary materials for further research, providing users with suggestions for future research directions after publication. The compatibility between extended resources and core resources is indicated; for example, the peer review comment collection is adapted to the peer review response guide, helping users better understand the types of peer review comments.

[0072] Step S1269: Based on the core resources of the modification and improvement stage after supplementing and extending resources, configure the supporting services for the modification and improvement stage module. The supporting services include modification guidance and consultation, interpretation of review comments, suggestions for optimizing results and consultation on secondary publication, and supplement service process details.

[0073] Based on the core resources of the revision and improvement stage after supplementing and extending the resources, this module is equipped with supporting services. These services include revision guidance and consultation, where subject matter experts provide one-on-one guidance on paper revision; peer review comment interpretation services to help users accurately understand the meaning and requirements of peer review comments; optimization suggestions for the paper's content, structure, and expression; and secondary publication consultation, such as suggestions for resubmitting to other journals or conferences if the user's paper is rejected. The supplementary service process details, such as the revision guidance and consultation process, include the user submitting the revised draft and peer review comments, scheduling a consultation with experts, providing online or offline guidance, and providing a revision report.

[0074] Step S12610: Integrate the core resources, extended resources, and supporting services of the experimental verification stage module, the results writing stage module, and the modification and improvement stage module to form a resource service combination for each resource service module, and mark the resource connection relationship and service differences between modules.

[0075] The core resources, extended resources, and supporting services of the experimental verification, results writing, and revision / refinement modules are integrated to form a complete resource service package for each module. The resource connections between modules are clearly indicated; for example, the experimental results storage service in the experimental verification phase is linked to the reference organization service in the results writing phase, allowing experimental data to be cited as references. Service differences are also clearly defined; for example, the supporting services in the experimental verification phase focus on supporting the experimental process, the supporting services in the results writing phase focus on supporting paper writing, and the supporting services in the revision / refinement phase focus on improving paper quality.

[0076] Step S127: Connect the resource service combinations of each research stage module, mark the resource connection relationship and service transition method between each research stage module, and form a phased subject resource service architecture framework. Based on the phased subject resource service architecture framework, add a resource update mechanism and service adjustment rules to it. The resource update mechanism includes the update cycle of resources of each resource service module, and the service adjustment rules include the adaptation and adjustment conditions of services. Integrate the phased subject resource service architecture framework with added resource update mechanism and service adjustment rules into a phased subject resource service architecture, output the phased subject resource service architecture, and synchronously store it in the resource service management module.

[0077] The resource service packages for the five research phases—topic selection, theoretical research, experimental verification, and results writing and revision—are linked sequentially, with the resource connections between modules clearly marked. For example, review articles in the topic selection phase provide the research background for core journal articles in the theoretical research phase. Service transition methods are clearly defined; for instance, when transitioning from document delivery services in the theoretical research phase to experimental resource reservation services in the experimental verification phase, subject librarians proactively remind users of the service switch. A phased subject resource service architecture framework is formed, incorporating a resource update mechanism that specifies the update cycle for each resource service module (e.g., core journal articles are updated monthly, and subject monographs are updated annually). Service adjustment rules are added, setting adaptation and adjustment conditions for services; for example, if user demand for a certain type of service decreases by 20% for three consecutive months, that service will be optimized or adjusted. The framework with added resource update mechanisms and service adjustment rules is integrated into the final phased subject resource service architecture, which is then output and synchronously stored in the resource service management module.

[0078] Step S130: Connect the phased subject resource service architecture, integrate the resource service modules according to the research progress logic, and generate a phased subject service recommendation list. The phased subject service recommendation list carries the resource details and connection guidance for each phase.

[0079] The various resource service modules within the phased subject resource service architecture are integrated according to the progression logic of research by users in the field of computer science and technology: topic selection and research, theoretical research, experimental verification, results writing, and revision and improvement. During the integration process, seamless connection between resource services in each module is ensured, generating a phased subject service recommendation list. This list includes resource details for each research stage, such as resource name, access method, and content summary, as well as transition guidance, such as switching prompts for key resources and services when transitioning from the theoretical research stage to the experimental verification stage.

[0080] Step S131: Retrieve the phased subject resource service architecture from the resource service management module, parse the resource service combination, connection relationship and adaptation rules of each resource service module in the phased subject resource service architecture, and extract the core resource identifier and service code of each resource service module.

[0081] The phased subject-specific resource service architecture is retrieved from the resource service management module and analyzed to clarify the resource service combinations of each module, i.e., the core resources, extended resources, and supporting services included in each module. The connections between modules are analyzed, such as resource citation relationships and service transition methods. Adaptation rules are understood, such as the matching conditions between resource services and research stages. Based on the analysis results, core resource identifiers for each resource service module are extracted, such as DOI numbers for documents and ISBN numbers for books, as well as service codes. For example, a specific service code is assigned to the topic selection consultation service to uniquely identify the service.

[0082] Step S132: Based on the core resource identifier and service code obtained from the parsing, connect each resource service module according to the research progress logic to generate a module progress sequence and determine the module connection order from the topic selection and research stage to the modification and improvement stage.

[0083] Based on the core resource identifiers and service codes obtained through parsing, and following the logical progression of user research in the field of computer science and technology, the various resource service modules are linked together to generate a module progression sequence. This sequence clearly defines the order of progression, starting with the topic selection and research phase, then moving through the theoretical research phase, experimental verification phase, results writing phase, and finally the revision and improvement phase, ensuring that the advancement of resource service modules is consistent with the development of the research process.

[0084] Step S133: Based on the module advancement sequence, extract the core resources, extended resources and supporting services of each resource service module, sort the resource service content according to the importance of the research process, and prioritize the core resources and key services.

[0085] Based on the module progression sequence, the core resources, extended resources, and supporting services of each resource service module are extracted one by one. These resource services are then prioritized according to their importance in the research process. Core resources, which are crucial for supporting this research phase, should be given priority. Key services, such as topic selection consultation and experimental design optimization guidance, which play a vital role in advancing the research process, are also prioritized. The prioritization considers factors such as the role of the resource services in the research phase and the urgency of user needs.

[0086] Step S1331: Analyze each resource service module in the module advancement sequence and extract the core resource list, extended resource list and supporting service list corresponding to each resource service module.

[0087] Each resource service module in the module advancement sequence is analyzed, and a detailed core resource list for each module is extracted. For example, the core resource list for the topic selection and research phase module includes specific review articles and cutting-edge research reports; the extended resource list includes relevant policy interpretation materials; and the supporting service list includes topic selection consultation services and literature retrieval training services. This ensures that the list accurately reflects the resource service composition of the module.

[0088] Step S1332: Retrieve the research progress characteristics of the subject users from the subject service data center, extract the research stage importance evaluation corresponding to each resource service module, and determine the importance weight of the resource service by combining the frequency of resource use, demand matching degree and service-related feedback.

[0089] The system retrieves research progress characteristics of users within a specific discipline from the subject service data center, extracting an importance assessment of each research stage corresponding to each resource service module. For example, the theoretical research stage is rated as highly important in the overall research process. Combining this with factors such as resource usage frequency (how often users use each resource within the module), demand matching degree (the degree to which resource services align with user needs), and service-related feedback (such as user satisfaction ratings for supporting services), the system comprehensively analyzes and determines the importance weight of each resource service. This weight value reflects the relative importance of the resource service within the module.

[0090] Step S1333: Based on the determined importance weight of resource services, sort the core resource list, prioritize the core resources with higher weights, and mark the core value and applicable scenarios of the resources.

[0091] The core resource list is sorted according to the determined importance weights of the resource services. Core resources with higher importance weights are ranked first; for example, in the theoretical research phase module, the core journal articles with the highest weights are ranked first in the core resource list. The core value of each ranked core resource is then labeled. For example, the core value of a core journal article might be that it proposes a new algorithmic model and its applicable scenarios, such as its suitability for solving specific types of theoretical problems.

[0092] Step S1334: Based on the ranking results and importance weights of the core resource list, sort the extended resource list according to the strength of association with the core resources and the importance weights. Extended resources with close association and leading weights follow the corresponding core resources, and supplement the association descriptions.

[0093] Based on the ranking results and importance weights of the core resource list, the extended resource list is ranked. The ranking primarily considers the strength of the correlation between extended resources and core resources, prioritizing those with higher correlation; it also considers the importance weights of the extended resources themselves. Extended resources with close correlations and higher weights are placed after their corresponding core resources; for example, statistical analysis literature (extended resources) related to a core journal article is placed after it. Supplementary explanations of the correlation are provided, such as how the extended resource assists in understanding and applying the core resource.

[0094] Step S1335: Based on the ranking results of the extended resource list, organize the supporting service list, screen the services that are closely related to the use of core resources and the progress of research, and sort them according to the importance and frequency of use, giving priority to services that are more important and used more frequently.

[0095] Based on the ranking results of the extended resource list, a list of supporting services was compiled. Services closely related to the use of core resources and the advancement of research progress were selected, such as document delivery services related to the use of core journal papers and experimental protocol optimization guidance services related to the advancement of research progress. The selected supporting services were ranked according to their importance, such as their impact on the research process, and their frequency of use (i.e., how often users use the service). Services with higher importance and higher usage frequency were given priority; for example, topic selection consultation services were prioritized in the topic selection research phase module.

[0096] Step S1336: Based on the sorting results of the supporting service list, supplement the explanation of the basis for the sorting of resource services, and mark the source of the importance weight and sorting rules of each resource service.

[0097] Based on the ranking results of the supporting service list, supplement the explanation of the basis for ranking resource services. Label the source of the importance weight for each resource service, such as calculating it based on 40% resource usage frequency, 30% demand matching degree, and 30% service feedback; clarify the ranking rules, such as ranking according to importance weight from high to low, and if weights are the same, ranking according to usage frequency from high to low.

[0098] Step S1337: Integrate the sorted core resource list, extended resource list, and supporting service list to form an ordered resource service combination for each resource service module. Add identifiers to the core resources and key services prioritized in the ordered resource service combination, associate the ordered resource service combinations of the preceding and following modules, supplement the connection relationship between the core resources of the preceding module and the core resources of the following module, output the ordered resource service combination of each resource service module, and synchronously store it in the recommended content management module.

[0099] The sorted lists of core resources, extended resources, and supporting services are integrated to form an ordered resource service combination for each resource service module. Key resources and services prioritized within the combination are clearly identified, such as using specific symbols or colors, for easy user identification. The ordered resource service combinations of preceding and following modules are linked, clarifying the connection between core resources in one module and those in the next; for example, review articles in the topic selection and research phase provide research background support for core journal articles in the theoretical research phase. The ordered resource service combination for each module is then output and synchronously stored in the recommendation content management module.

[0100] Step S134: Integrate the resource service content of each sorted resource service module, supplement the resource transition description between resource service modules, generate the connection value and usage suggestions between the previous stage resources and the next stage resources, form a preliminary recommendation list framework, and based on the preliminary recommendation list framework, supplement the detailed information of each resource dimension, including the collection location, online access link, usage permissions, content summary and applicable scenarios.

[0101] The resource service content of each sorted resource service module is integrated into a coherent whole. Supplementary explanations are provided regarding resource transitions between modules, outlining the reasons and logic for moving from one module's resources to the next. The value of connecting resources from previous and subsequent stages is generated, such as how cutting-edge research reports in the topic selection phase lay the groundwork for understanding core journal articles in the theoretical research phase, along with usage suggestions, such as referring to relevant cutting-edge research reports before reading core journal articles. A preliminary recommendation list framework is established, upon which detailed information on each resource dimension is added, including its location in the library (e.g., the specific floor and shelf number of a physical book); online access links (e.g., the database access address for electronic documents); access permissions (e.g., whether access is limited to on-campus use or requires download permission); a content summary briefly introducing the main content; and applicable scenarios (e.g., whether the resource is suitable for course paper writing or scientific research projects).

[0102] Step S135: Based on the preliminary recommendation list framework after supplementing the resource dimensions, add detailed guidance for supporting services. The detailed guidance includes service activation conditions, appointment process, service duration, contact information, and service delivery format, supplementing the service dimension content.

[0103] Building upon the initial recommendation list framework supplemented with resource dimensions, detailed guidance on supporting services has been added. This guidance includes service activation conditions, such as the requirement for certain advanced services to provide proof of specific research projects; the appointment process, including the specific steps for online appointment and the required information; service duration, such as a one-hour limit for a single consultation; contact information, such as the service provider's phone number, email address, and office location; and the delivery format of service deliverables, such as consultation reports delivered via email in PDF format, and training courses providing video replay links. By adding this detailed guidance, the service dimensions are supplemented, enabling users to clearly understand how to access and use the supporting services.

[0104] Step S136: The preliminary recommended list framework after splitting the supplementary service dimensions according to the research stage is a phased sub-list. Each phased sub-list corresponds to the resource service content of a research stage, and the stage identifier and progress node are marked.

[0105] Based on the research phases, the initial recommendation list framework, supplemented with service dimensions, is divided into five phased sub-lists, corresponding to the topic selection and research, theoretical research, experimental verification, and results writing and revision phases, respectively. Each phased sub-list contains all resource service content for that research phase, and is marked with a phase identifier, such as "Topic Selection and Research Phase Sub-list," and progress milestones are marked, such as this sub-list should be pushed out starting in the second week after the user's enrollment and ending in the eighth week.

[0106] Step S137: Connect the sub-lists of each stage, add research progress prompts, generate key points for resource service connection between the current stage and the next stage, form an initial stage-specific subject service recommendation list, divide the initial stage-specific subject service recommendation list into display sections according to resource type and service importance, so that each section corresponds to a type of resource or a type of service, generate a stage-specific subject service recommendation list, output the stage-specific subject service recommendation list, and synchronously store it in the recommendation content management module.

[0107] The sub-lists of each stage are linked together in chronological order of research phases, with progress indicators added, such as "You have entered the theoretical research stage; the following are recommended resource services for you." Key points for connecting the current stage with the next stage's resource services are generated; for example, adding "We are about to enter the experimental verification stage; we recommend paying attention to experimental method literature and experimental resource reservation services in advance" to the end of the theoretical research stage sub-list. An initial stage-specific subject service recommendation list is formed, divided into sections based on resource type (books, journal articles, software tools, etc.) and service importance (critical services, general services, etc.). Each section corresponds to a type of resource or service, such as "Core Journal Articles Section" and "Experiment-Related Services Section." The final stage-specific subject service recommendation list is generated, output, and simultaneously stored in the recommendation content management module.

[0108] Step S140: Collect research progress data of subject users in real time, dynamically adjust the recommended list of subject services for each stage, optimize the module connection logic, supplement transitional resources and services during the turning point of the research process, and form dynamic subject service recommendation content.

[0109] The library's user behavior monitoring system collects real-time research progress data from users in the computer science and technology fields. Based on this data, the recommended list of subject services is dynamically adjusted. The connection logic between resource service modules is optimized to better match the actual research progress of users. When a user enters a turning point in their research process, transitional resources and services are added to help them transition smoothly. Ultimately, this results in dynamic subject service recommendations that ensure the recommendations adapt to changes in the user's research progress in real time.

[0110] Step S141: Collect research progress data of subject users in real time. The research progress data of subject users includes resource usage behavior, changes in consultation content, research stage switching indicators and output progress.

[0111] The system collects research progress data from users in the discipline in real time through various data acquisition methods. This data includes resource usage behavior, such as users clicking, downloading, borrowing, and collecting recommended resources; changes in consultation content, such as the differences between the latest consultation questions and previous consultation questions; research stage switching indicators, such as users manually marking the entry into a new research stage in the system, or stage switching signals automatically identified by the system based on resource usage behavior; and progress of output, such as the writing progress of users' papers and the status of patent applications.

[0112] Step S142: Retrieve the phased subject service recommendation list from the recommended content management module, parse the module connection logic of the phased subject service recommendation list, and combine it with the updated research stage information to determine the research stage of the subject user and whether it has entered a transition period. The transition period refers to the period of stage transition and research direction adjustment.

[0113] The system retrieves a phased list of subject-specific service recommendations from the recommended content management module, analyzes the module connection logic of this list, and understands the relationships and transition methods between various resource service modules. Combined with real-time collected and updated research stage information, such as the user's latest resource usage behavior focusing on experimental data processing resources, it determines the current research stage of the subject user, such as the experimental verification stage. It also determines whether the user has entered a transitional period, including stage transitions, such as from theoretical research to experimental verification, and research direction adjustments, such as when a user shifts from machine learning to deep learning.

[0114] Step S143: For discipline users who are not in a transitional period, adjust the priority of resource services in the corresponding research stage sublist based on the judgment results, and adjust the order of frequently used resources and services in the current stage.

[0115] For users in disciplines that are not in a transitional phase, the priority of resource services in the sub-list for that phase is adjusted based on the determined current research stage. The user's resource usage behavior at the current stage is analyzed to identify frequently used resources and services. These frequently used resources and services are then moved up in the order of their sub-lists for easier access by the user. For example, if a user frequently uses certain monographs during the theoretical research phase, the position of these monographs in the core resource list is moved up.

[0116] Step S144: For users of disciplines entering a transitional period, extract the core resources and services of the modules before and after the research phase, analyze the common characteristics of the resources and services before and after the phase, and screen the transitional resources that are suitable for both phases. The transitional resources include cross-resources, connecting literature, and comprehensive services.

[0117] When a discipline is identified as entering a transitional phase, the core resources and services of the modules preceding and following this phase are extracted. The common characteristics of these resources and services are analyzed, such as the relevance of resource themes and the similarity of service objectives. Based on these common characteristics, transitional resources suitable for both phases are selected. Cross-cutting resources are those that integrate research content from both phases, such as literature involving both theoretical models and experimental designs. Bridging literature summarizes the research findings of the previous phase and guides the research direction of the next phase. Comprehensive services meet the common needs of both phases, such as guidance on using literature management tools.

[0118] Step S1441: Based on the results of the transition period obtained from the judgment, determine the two research stages before and after the transition period, retrieve the core resource list, extended resource list and supporting service list of the modules in these two stages, and determine the adaptation characteristics and core value of various resource services.

[0119] Based on the identified turning point, clarify the two research stages preceding and following the turning point, such as the transition from the theoretical research stage to the experimental verification stage. Retrieve the core resource lists, extended resource lists, and supporting service lists for these two stages, and analyze in detail the suitability characteristics of each resource service. For example, the suitability characteristic of core resources in the theoretical research stage is high theoretical depth, while the suitability characteristic of core resources in the experimental verification stage is strong practicality. Determine the core value of each resource service; for example, the core value of core journal articles in the theoretical research stage is providing cutting-edge theoretical perspectives, while the core value of experimental methodological literature in the experimental verification stage is guiding experimental operations.

[0120] Step S1442: Analyze the adaptation characteristics and core value of resource services in the two stages, and extract resource types that are adaptable to both stages. The resource types include interdisciplinary resources, cross-stage connecting literature, general tool materials, and comprehensive service types.

[0121] This study conducts an in-depth analysis of the adaptability characteristics and core values ​​of resource services in the two phases, identifying commonalities and points of connection, and extracting resource types that are adaptable to both phases. Interdisciplinary resources integrate knowledge from the disciplines of both phases and other related disciplines; cross-phase bridging literature connects the research content of the previous phase with the research needs of the next phase; general-purpose tools and materials are software tools, data processing templates, etc., that can be used in both phases; and comprehensive service types are services that can simultaneously meet the service needs of both phases, such as cross-phase literature consultation services.

[0122] Step S1443: Retrieve resources that meet common characteristics from the library's subject resource pool, and screen cross-disciplinary resources. The cross-disciplinary resources include literature that integrates the research content of the two stages, cross-disciplinary theoretical materials, and general experimental method manuals.

[0123] Resources meeting the above common characteristics are retrieved from the library's subject-specific resource pool, and cross-disciplinary resources are screened through content-theme matching, keyword retrieval, and other methods. Literature integrating research content from both stages involves both theoretical exploration in the previous stage and practical application in the subsequent stage; interdisciplinary theoretical materials combine theories from computer science and technology with those from other fields, suitable for the transitional period of interdisciplinary research; the experimental methods introduced in the general experimental methods manual have a certain degree of universality and can be applied to experimental research at different stages.

[0124] Step S1444: Based on the cross-resources obtained through screening, extract the connecting literature, which includes review articles that summarize the research results of the previous stage and guide the research direction of the next stage, interim transitional research reports, and methodological connecting materials.

[0125] Based on the selected cross-disciplinary resources, further connecting literature is extracted. Review articles summarizing the findings of the previous stage and guiding the direction of the next stage help users organize their preliminary research and clarify future directions; transitional research reports are specialized reports for transitioning between research stages, providing ideas and suggestions for the transition; methodological connecting materials introduce how to apply the research methods of the previous stage to the next stage, or how to switch from one research method to another.

[0126] Step S1445: Based on the extracted transitional documents, screen the comprehensive service types, determine the service's applicable scope and content, calculate the correlation strength between the screened transitional resources and the core resources of the preceding and following stages, retain transitional resources with correlation strength higher than a preset threshold, and form a transitional resource list.

[0127] Based on the extracted transitional literature and the service needs of both the preceding and following stages, comprehensive service types are selected. The scope of application for these comprehensive services is determined, such as their applicability to all users transitioning from theoretical research to experimental verification, and their specific content, such as experimental design consultation and guidance on the application of theoretical models. The correlation strength between the selected transitional resources and the core resources of both stages is calculated, using indicators such as resource topic similarity and citation relationships. Transitional resources with correlation strengths exceeding a preset threshold are retained, forming a list of transitional resources.

[0128] Step S1446: Based on the formed transitional resource list, supplement the detailed information of the resources, including the resource name, collection location, acquisition channel, content summary, adaptation stage and related descriptions of resources in the preceding and following stages, and supplement the resource dimensions.

[0129] Based on the list of transitional resources, supplement each transitional resource with detailed information. The resource name accurately reflects the resource's subject matter; the location clearly indicates where the printed resource is stored; the access channels provide ways to access the resource both online and offline; the content summary concisely summarizes the core content of the resource; the adaptation stage indicates the transitional period before and after which the resource is primarily adapted; and the connection to resources in the preceding and following stages explains how the transitional resource connects to the core resources of those stages. By supplementing this information, the resource dimensions are improved.

[0130] Step S1447: Based on the supplemented resource dimension, sort the transition resource list according to the degree of correlation with the preceding and following stages, and prioritize transition resources with a balanced degree of correlation. Based on the sorted transition resource list, associate the transition resources with the corresponding comprehensive services to form a combination unit of transition resources and comprehensive services. Each combination unit corresponds to a type of transition requirement and service support. Output the transition resource list and the combination unit, and store them synchronously in the dynamic recommendation database.

[0131] Based on the supplemented resource list, transitional resources are sorted according to their relevance to both the preceding and following stages, with a balanced relevance ranking—that is, transitional resources with a high relevance to both stages are prioritized. Based on this sorted list, transitional resources are associated with corresponding integrated services, forming combined units of transitional resources and integrated services. Each combined unit addresses a specific type of transitional need, such as the transition from theory to experiment, and provides corresponding service support, such as experimental method training services. The transitional resource list and combined units are output and simultaneously stored in a dynamic recommendation database.

[0132] Step S145: Based on the selected transitional resources, supplement the transitional period-specific services, which include research phase transition guidance, resource switching suggestions, direction adjustment consultation, and cross-phase service connection, thus supplementing the transitional period service content.

[0133] Based on the selected transitional resources, supplementary services are provided for users entering the transition period. These include: a research phase transition guidance service to help users understand the differences between the two research phases and key points of the transition; a resource switching advice service to provide methods and techniques for switching from resources used in the previous phase to those used in the next; a direction adjustment consultation service to offer professional advice on potential adjustments to research direction; and a cross-phase service bridging service to ensure seamless service continuity during the phase transition. These exclusive services supplement the transition period services to meet the specific needs of users during this transitional period.

[0134] Step S146: Combining the supplementary transition period-specific services and transition resources, adjust the connection content of the sub-lists of the preceding and following stages based on the supplementary transition period-specific services and transition resources, and add a description of the relationship between the transition resources and the resources of the preceding and following stages.

[0135] The supplementary transitional services and resources will be integrated into the sub-lists for the preceding and following stages, and the connection content of the sub-lists will be adjusted. At the junction of the sub-lists for the preceding and following stages, explanations will be added regarding the relationship between the transitional resources and the resources of the preceding and following stages. For example, it may be explained that a certain bridging document summarizes the core journal articles of the previous stage and guides the experimental method literature of the following stage, helping users understand the role and usage of the transitional resources and achieving a smooth transition of resource services.

[0136] Step S147: Integrate the adjusted phased sub-lists, transitional resources, and transitional services to form a dynamically adjusted initial recommendation list, mark the basis for adjustment and the characteristics of the research stage, and based on the dynamically adjusted initial recommendation list, supplement the explanation of the dynamic update mechanism, and determine the triggering conditions, update cycle, and content adjustment rules for the list update.

[0137] Integrate the adjusted phased sub-lists, supplementary transitional resources, and services specific to transitional periods to form a dynamically adjusted initial recommendation list. The list should indicate the basis for adjustments, such as adjustments based on users' research phase switching behavior, and the characteristics of the current research phase, such as being in the transition from theoretical research to experimental verification. A supplementary explanation of the dynamic update mechanism should be provided, clarifying the triggering conditions for list updates, such as changes in users' research phases or the emergence of new transitional resources; setting an update cycle, such as checking update conditions daily and updating when conditions are met; and establishing content adjustment rules, such as prioritizing the addition of new resources to the corresponding module's extended resource list.

[0138] Step S148: Arrange the contents of the initial recommendation list according to the research progress order, add stage progress prompts and subsequent resource service previews, integrate and optimize the initial recommendation list, dynamic update mechanism description and progress prompt content to form dynamic subject service recommendation content, output the dynamic subject service recommendation content, and synchronously store it in the dynamic recommendation database.

[0139] The initial recommendation list is arranged according to the progress of the research, ensuring logical coherence. Progress indicators are added, such as "You are currently in the middle of the experimental verification phase and have completed 60% of the recommended resource learning"; previews of subsequent resource services are added, such as "In the next phase, you will enter the results writing phase, at which time we will recommend resource services such as paper writing guidelines." The optimized initial recommendation list, explanation of the dynamic update mechanism, and progress indicators are integrated to form the final dynamic subject-specific service recommendation content, which is then output and synchronously stored in the dynamic recommendation database.

[0140] Step S150: Output the recommended content of the dynamic subject service, collect process adaptation feedback data, integrate the process adaptation feedback data into the library subject service big data, and optimize the subject user research process characteristics and the dynamic adaptation relationship based on the updated library subject service big data.

[0141] The library's subject service platform delivers dynamic subject service recommendations to users, such as displaying them on their personal library account pages and sending emails. Simultaneously, it collects user feedback data on the adaptation process of these recommendations. This feedback data is integrated into the library's subject service big data system, and the updated big data is used to optimize the characteristics of subject users' research progress and dynamic adaptation relationships, making the recommended content more accurately match user needs.

[0142] For example, step S151: Output the dynamic subject service recommendation content through the library subject service platform, and simultaneously record the output time, display format, subject user viewing behavior, resource click status and service reservation status.

[0143] Dynamic subject service recommendations are disseminated through various channels of the library's subject service platform, such as the platform's homepage recommendation section, personalized recommendation pages, and mobile app push notifications. Output-related information is recorded synchronously, with output time accurate to the minute; display format (list, card, or pop-up); subject user viewing behavior, including whether users viewed recommended content and the duration of viewing; resource click data, recording which recommended resources users clicked; and service reservation data, recording whether users reserved recommended complementary services, the type of service reserved, and the time.

[0144] Step S152: Based on the output dynamic subject service recommendation content and the recorded relevant information, collect process adaptation feedback data. The process adaptation feedback data includes subject users' satisfaction evaluation of the recommended content, resource usage feedback, service experience suggestions, supplementary explanations of needs, and research stage adjustment feedback.

[0145] Based on the output of dynamic subject service recommendations and related information such as output time and user viewing behavior, process adaptation feedback data is collected through multiple methods. A satisfaction rating button is set up on the recommended content page, allowing users to rate or provide textual feedback on the recommended content; resource usage feedback is analyzed through user behavior, such as reading time after downloading and whether the content is saved; user suggestions on supporting services are collected, such as service response speed and service quality; user-submitted supplementary requests are received, such as users believing that a specific type of literature is missing from the recommended resources; and user feedback on adjustments to their research stage is obtained, such as users manually correcting the research stage determined by the system.

[0146] Step S153: Classify and organize the process adaptation feedback data, divide the storage directory according to data type, extract core information based on the classified and organized process adaptation feedback data, analyze the adaptation evaluation, feedback problems and new demand directions of subject users for resource services at each stage, and refine the adaptation optimization direction.

[0147] The collected process adaptation feedback data is categorized and organized according to data type, such as satisfaction evaluation data, resource usage feedback data, and service experience suggestion data, for easy retrieval and analysis later. Based on the categorized data, core information is extracted, such as the average user satisfaction score and frequently reported resource issues. The adaptation evaluation of resource services at each stage by subject-specific users is analyzed, such as which stages have high and low resource adaptation rates; feedback issues are summarized, such as broken resource access links and difficulties in service booking; new areas of demand are identified, such as users requesting more interdisciplinary resources; based on these analyses, optimization directions are extracted, such as optimizing resource link effectiveness, improving the service booking process, and increasing interdisciplinary resource recommendations.

[0148] Step S154: Import the categorized and organized process adaptation feedback data into the library's subject service big data to supplement subject users' borrowing records, consultation records, resource usage trajectories, and output records.

[0149] The categorized and organized process adaptation feedback data is imported into the library's subject service big data system and integrated with the existing data. This feedback data supplements subject users' borrowing records, such as borrowing information for recommended books; it also supplements consultation records, such as new consultation questions arising during the use of recommended services; it enriches resource usage patterns, such as clicks, downloads, and collections of recommended resources; and it improves output records, such as the publication of papers by users after using recommended resource services. By supplementing the data, the completeness and timeliness of the library's subject service big data are enhanced.

[0150] Step S155: Using the supplemented library subject service big data and the refined adaptation and optimization directions as input, update the research stage division, demand evolution characteristics and resource usage trajectory analysis results in the research process characteristics of the subject users, and add profile dimensions corresponding to the newly added demands.

[0151] Using the supplemented big data on library subject services and the refined optimization directions as input, the characteristics of subject user research progress are updated. The accuracy of the research phase divisions is reassessed, such as adjusting the time nodes for each phase based on user feedback; the characteristics of demand evolution are updated to reflect the latest trends in user demand changes; and the results of resource usage trajectory analysis are revised to better align with actual user resource usage behavior. Simultaneously, based on new demands reported by users, corresponding profile dimensions are added to the characteristics of subject user research progress, such as a new "interdisciplinary research demand" profile dimension, to more accurately depict user needs.

[0152] Step S156: Using the updated subject user research progress characteristics as input, adjust the adaptation weights of each research stage and resource service in the dynamic adaptation relationship, and add resource service adaptation rules corresponding to the new requirements.

[0153] The updated characteristics of user research progress across disciplines are used as input to optimize the dynamic matching relationship. The matching weights between each research stage and resource services are adjusted; for example, if user feedback indicates a high matching rate for core journal articles in the theoretical research stage, their matching weight is increased; conversely, the matching weight for resource services with lower user ratings is decreased. For new needs, such as interdisciplinary research needs, corresponding resource service matching rules are added, stipulating that when users have interdisciplinary research needs, interdisciplinary resources and services are prioritized.

[0154] Step S157: Using the adjusted dynamic adaptation relationship as input, update the service process details and connection timing in the service adaptation content, and add the missing adaptation content.

[0155] Using the adjusted dynamic adaptation relationship as input, update the service adaptation content. Refine service process details, such as simplifying the appointment process and reducing the number of information items to be filled in based on user feedback regarding its complexity; optimize the timing of connections, such as starting to push service previews for the experimental verification phase a week in advance when users are about to complete the theoretical research phase. Check the service adaptation content and add previously missing adaptation content, such as supplementing a detailed process for the second publication consultation service during the modification and improvement phase.

[0156] Step S158: Record the updated content, update time, and data source of the library's subject service big data, the research progress characteristics of the subject users, and the dynamic adaptation relationship, and generate an update log.

[0157] Detailed records should be kept of updates to the library's subject-specific service big data, the characteristics of subject-specific user research progress, and dynamic adaptation relationships. This includes details such as newly added user feedback data in the big data, adjustments to research progress characteristics, and modifications to adaptation weights in the dynamic adaptation relationships. The exact time of each update should be recorded, accurate to the second; the data source should be noted, such as whether the updated content comes from user satisfaction feedback data or resource usage trajectory data. This information should be compiled into an update log for traceability and auditing.

[0158] Step S159: Based on the updated library subject service big data, the subject user research process characteristics, and the dynamic adaptation relationship, optimize the subsequent subject user research process characteristic construction, phased subject resource service architecture construction, and recommended content generation process.

[0159] By leveraging updated big data on library subject services, characteristics of subject user research progress, and dynamic adaptation relationships, subsequent related processes are optimized. In the process of constructing subject user research progress characteristics, a new feedback data processing algorithm is introduced to improve the accuracy of feature construction. In the process of building a phased subject resource service architecture, module division and resource service combinations are adjusted based on updated dynamic adaptation relationships. In the process of generating recommended content, the ranking algorithm is optimized to make recommended content more aligned with users' actual needs and feedback. Through continuous optimization, the quality and effectiveness of intelligent recommendations for library subject services are constantly improved.

[0160] Figure 2 The illustration shows the hardware structure of a big data mining-based intelligent recommendation system 100 for implementing the above-described big data mining-based intelligent recommendation method for library subject services, as provided in an embodiment of this application. Figure 2As shown, the intelligent recommendation system 100 for library subject services based on big data mining may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.

[0161] Machine-readable storage medium 120 can store data and / or instructions. In a specific implementation, one or more processors 110 execute the computer-executable instructions stored in the machine-readable storage medium 120, enabling the processor 110 to execute the intelligent recommendation method for library subject services based on big data mining as described in the above method embodiment. The processor 110, the machine-readable storage medium 120, and the communication unit 140 are connected via a bus 130, and the processor 110 can be used to control the sending and receiving operations of the communication unit 140.

[0162] The specific implementation process of processor 110 can be found in the various method embodiments executed by the above-mentioned intelligent recommendation system for library subject services based on big data mining. The implementation principle and technical effect are similar, and will not be repeated here.

[0163] Furthermore, this application embodiment also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned intelligent recommendation method for library subject services based on big data mining is implemented.

[0164] It should be noted that, in order to simplify the description disclosed in this application and thus help to understand one or more embodiments of the invention, the foregoing description of the embodiments of this application may sometimes combine multiple features into one embodiment, drawing or description thereof.

Claims

1. A method for intelligent recommendation of library subject services based on big data mining, characterized in that, The method includes: By mining big data on library subject services, constructing the research process characteristics of subject users, retrieving the library's subject resource pool, and establishing a dynamic adaptation relationship between research process and resources in combination with the library's subject resource pool, the research process characteristics of subject users cover the research stages of subject users, resource usage trajectory, and demand evolution characteristics. Based on the dynamic adaptation relationship, the resource service modules are divided according to the research process stages of the user discipline. Each resource service module corresponds to the core resource needs and supporting services of a research stage, forming a phased discipline resource service architecture. By connecting the aforementioned phased subject resource service architecture and integrating resource service modules according to the research progress logic, a phased subject service recommendation list is generated, which carries the resource details and connection guidelines for each phase. Real-time collection of research progress data from subject users, dynamic adjustment of the phased subject service recommendation list, optimization of module connection logic, supplementation of transitional resources and services during research progress transition periods, and formation of dynamic subject service recommendation content; Output the recommended content of the dynamic subject service, collect process adaptation feedback data, integrate the process adaptation feedback data into the library subject service big data, and optimize the research process characteristics of the subject users and the dynamic adaptation relationship based on the updated library subject service big data.

2. The intelligent recommendation method for library subject services based on big data mining according to claim 1, characterized in that, The process of mining big data from library subject services, constructing research process characteristics of subject users, retrieving library subject resource pools, and establishing a dynamic adaptation relationship between research process and resources based on the library subject resource pools includes: The library retrieves subject service big data and extracts basic information of subject users, resource borrowing records, document download trajectory, consultation interaction content, research project identification and output records from the subject service big data. Invalid data without research focus is removed, and subject user research stage characteristics are extracted from the extracted valid data. The subject user research stage characteristics include topic selection and research stage, theoretical research stage, experimental verification stage, result writing stage and revision and improvement stage. The time nodes and resource usage characteristics of each stage are marked. By connecting the resource usage trajectories of each research stage, analyzing the switching patterns of resource types, changes in usage frequency, and evolution of consultation questions in the resource usage trajectories, and combining the marked stage time nodes and resource usage characteristics, we can summarize the evolution characteristics of user needs in the discipline and form a demand evolution path. By integrating the research stage characteristics, resource usage trajectory, and demand evolution path, and classifying them by subject area and research direction, we construct the initial subject user research process characteristics, with each initial subject user research process characteristic corresponding to a subject user research mode. Retrieve the library's subject resource pool, extract the subject attributes, applicable research stages, content focus, and supporting service descriptions of the resources in the library's subject resource pool, and establish resource stage adaptation tags, with each resource corresponding to at least one research stage tag; The initial subject user research process characteristics are associated and matched with the resource stage adaptation tags. Resource tags corresponding to each research stage are extracted and labeled as process-related resource tags. Based on the frequency of use and demand matching degree of the process-related resource tags in each research stage, a dynamic adaptation relationship between research process and resources is established, and the core resources and extended resources of each research stage are determined. The service adaptation content in the dynamic adaptation relationship is supplemented to determine the corresponding service type, service process and connection time for each research stage. After supplementing the adaptation relationship dimension, the dynamic adaptation relationship is integrated into the initial discipline user research process characteristics. The adaptation rules and demand evolution adaptation logic are supplemented to form the discipline user research process characteristics. The discipline user research process characteristics and the dynamic adaptation relationship are output and synchronously stored in the discipline service data center.

3. The intelligent recommendation method for library subject services based on big data mining according to claim 1, characterized in that, Based on the dynamic adaptation relationship, the resource service modules are divided according to the research process stages of the user in each discipline. Each resource service module corresponds to the core resource needs and supporting services of a research stage, forming a phased discipline resource service architecture, including: The research process characteristics of the subject users and the dynamic adaptation relationship are retrieved from the subject service data center. The resource service modules are divided into topic selection and research stage, theoretical research stage, experimental verification stage, result writing stage, and revision and improvement stage. Each resource service module corresponds to a research stage. The module name and core service objectives of each resource service module are marked. For the topic selection and research phase module, the core resources corresponding to the topic selection and research phase in the dynamic adaptation relationship are retrieved. The core resources include subject review literature, cutting-edge research reports, topic selection reference materials and relevant policy documents. The basic information of the resources and their acquisition channels are extracted. Based on the extracted core resource information and acquisition channels of the topic selection and research phase, supplementary services for the topic selection and research phase module are provided. These supplementary services include topic selection consultation services, literature retrieval training, resource navigation services, and research direction analysis services. The service content and appointment methods are determined. For the theoretical research stage module, the core resources corresponding to the theoretical research stage in the dynamic adaptation relationship are retrieved and integrated into discipline monographs, core journal articles, theoretical model literature and interdisciplinary theoretical materials, and supplemented with theoretical depth explanations and citation suggestions for the resources; Based on the integrated core resources and supplementary explanations for the theoretical research phase, supporting services for the theoretical research phase module are configured. These supporting services include document delivery services, interlibrary loan services, consultation services for theoretical difficulties, and guidance on the use of document management tools, as well as supplementary service process details. For the experimental verification stage, the results writing stage, and the revision and improvement stage modules, according to the configuration logic of the topic selection and research stage and the theoretical research stage modules, the core resources and extended resources of the corresponding stages in the dynamic adaptation relationship are retrieved to supplement the supporting services of each stage, so that each resource service module forms a resource service combination. By linking the resource service combinations of each research stage module, marking the resource connection relationships and service transition methods between each research stage module, a phased subject resource service architecture framework is formed. Based on the phased subject resource service architecture framework, a resource update mechanism and service adjustment rules are added to it. The resource update mechanism includes the update cycle of resources in each resource service module, and the service adjustment rules include the adaptation and adjustment conditions of services. The phased subject resource service architecture framework with the added resource update mechanism and service adjustment rules is integrated into a phased subject resource service architecture, and the phased subject resource service architecture is output and synchronously stored in the resource service management module.

4. The intelligent recommendation method for library subject services based on big data mining according to claim 1, characterized in that, The aforementioned phased subject resource service architecture integrates resource service modules according to the research progress logic, generating a phased subject service recommendation list, including: The phased subject resource service architecture is retrieved from the resource service management module. The resource service combination, connection relationship and adaptation rules of each resource service module in the phased subject resource service architecture are analyzed, and the core resource identifier and service code of each resource service module are extracted. Based on the core resource identifiers and service codes obtained from the analysis, the various resource service modules are connected according to the research progress logic to generate a module progression sequence and determine the module connection order from the topic selection and research stage to the modification and improvement stage. Based on the module advancement sequence, the core resources, extended resources and supporting services of each resource service module are extracted, and the resource service content is sorted according to the importance of the research process, with core resources and key services given priority. The resource service content of each resource service module after integration and sorting is supplemented with resource transition descriptions between resource service modules, generating the connection value and usage suggestions between the previous stage resources and the next stage resources, forming a preliminary recommendation list framework. Based on the preliminary recommendation list framework, detailed information of each resource dimension is supplemented, including collection location, online access link, usage permissions, content summary and applicable scenarios. Based on the initial recommendation list framework with supplemented resource dimensions, detailed guidance on supporting services is added. The detailed guidance includes service activation conditions, appointment process, service duration, contact information, and service delivery format, supplementing the service dimension content. The initial recommended list framework after splitting supplementary service dimensions according to research stage is a phased sub-list. Each phased sub-list corresponds to the resource service content of a research stage, and is marked with stage identifiers and progress nodes. By connecting the sub-lists of each stage and adding research progress prompts, key points for resource service connection between the current stage and the next stage are generated to form an initial stage-specific subject service recommendation list. The initial stage-specific subject service recommendation list is divided into display sections according to resource type and service importance, so that each display section corresponds to a type of resource or a type of service. The stage-specific subject service recommendation list is generated, output, and synchronously stored in the recommendation content management module.

5. The intelligent recommendation method for library subject services based on big data mining according to claim 1, characterized in that, The system collects real-time research progress data from users in specific disciplines, dynamically adjusts the phased list of recommended discipline services, optimizes module connection logic, and supplements transitional resources and services during research progress transitions, forming dynamic recommended discipline service content, including: Real-time collection of research progress data from subject users, including resource usage behavior, changes in consultation content, research stage switching indicators, and progress of output; The recommended list of phased subject services is retrieved from the recommended content management module. The module connection logic of the recommended list of phased subject services is analyzed. Combined with the updated research stage information, the research stage of the subject user is determined and whether it has entered a turning point. The turning point refers to the period of stage transition and adjustment of research direction. For users in disciplines that are not in a transitional period, the priority of resource services in the corresponding research stage sublist will be adjusted based on the judgment results, and the order of frequently used resources and services in the current stage will be adjusted. For users of disciplines entering a transitional period, the core resources and services of the modules before and after the research phase are extracted, the common characteristics of the resources and services before and after the phase are analyzed, and transitional resources that are suitable for both phases are selected. The transitional resources include cross-disciplinary resources, connecting literature, and comprehensive services. Based on the selected transitional resources, supplementary services specifically for the transition period are provided. These services include guidance for the research phase transition, suggestions for resource switching, consultation on direction adjustment, and cross-phase service connections, thus supplementing the service content for the transition period. Based on the supplementary transition period-specific services and transition resources, the connection content of the sub-lists of the preceding and following stages will be adjusted, and the relationship between the transition resources and the resources of the preceding and following stages will be added. The integrated and adjusted phased sub-lists, transitional resources, and transitional services form a dynamically adjusted initial recommendation list, marking the basis for the adjustment and the characteristics of the research stage. Based on the dynamically adjusted initial recommendation list, a description of the dynamic update mechanism is added, and the triggering conditions, update cycle, and content adjustment rules for the list update are determined. The contents of the initial recommendation list are arranged in the order of research progress, and stage progress prompts and subsequent resource service announcements are added. The integrated and optimized initial recommendation list, dynamic update mechanism description and progress prompts are combined to form dynamic subject service recommendation content, which is then output and synchronously stored in the dynamic recommendation database.

6. The intelligent recommendation method for library subject services based on big data mining according to claim 2, characterized in that, The resource usage trajectory of each research stage is analyzed, including the switching patterns of resource types, changes in usage frequency, and the evolution of consultation questions. Combined with marked stage time nodes and resource usage characteristics, the evolution characteristics of user needs in the discipline are summarized, forming a demand evolution path, including: Resource usage trajectory data for each research stage is extracted from the valid data after removing invalid data, and then strung together in chronological order to form a complete trajectory sequence. Each trajectory point corresponds to a resource usage behavior and associated information. Analyze the resource type switching in the complete trajectory sequence, statistically analyze the usage ratio and switching frequency of different types of resources in each research stage, summarize the type switching pattern, and determine the type evolution trend of resource demand. Based on the statistically obtained resource usage ratio and switching frequency, the usage frequency change curve of resource usage frequency in each research stage is calculated, and the time nodes, resource types and consultation content corresponding to the frequency peaks and troughs are extracted. The correlation between frequency changes and demand is analyzed. The consultation questions at each research stage were sorted out from the effective data, classified into a consultation question sequence according to the nature of the questions, and the evolution of questions from basic types to in-depth types and from resource acquisition types to outcome optimization types were analyzed. By combining the type switching pattern, the usage frequency change curve, and the consultation question sequence, the co-evolutionary characteristics among the type switching pattern, the usage frequency change curve, and the consultation question sequence are extracted to determine the core orientation and driving force of the needs at different research stages; Based on the characteristics of collaborative evolution and the core orientation of demand, we summarize the nodes of demand evolution of users in the research process. These nodes include demand upgrade nodes, demand transformation nodes, and demand expansion nodes. We also label the resource service characteristics corresponding to each node. Integrate the nodes of demand evolution, the trends of type evolution, and the driving forces of change, and form a framework for the demand evolution path according to the research stage sequence. Each stage corresponds to an evolution process and characteristic description. Based on the demand evolution path framework, we supplement it with adaptation suggestions and determine the direction of resource service adjustment for each evolution node. The content in the aforementioned demand evolution path framework is arranged in chronological order and according to evolution logic, and the relationships between each element are marked to generate a demand evolution path. The demand evolution path and corresponding adaptation suggestions are output and synchronously stored in the subject service data center.

7. The intelligent recommendation method for library subject services based on big data mining according to claim 3, characterized in that, The modules for the experimental verification stage, the results writing stage, and the revision and improvement stage, according to the configuration logic of the topic selection and research stage and the theoretical research stage modules, retrieve the core resources and extended resources corresponding to the stages in the dynamic adaptation relationship, supplement the supporting services for each stage, and make each resource service module form a resource service combination, including: For the experimental verification phase module, the core resources corresponding to the experimental verification phase in the dynamic adaptation relationship are retrieved. The core resources include experimental method literature, data manuals, instrument operation guides, experimental design cases and similar experimental research results. Basic information and usage scenario descriptions of the resources are extracted. Based on the extracted core resource information and usage scenario descriptions for the experimental verification phase, supplementary extended resources for the experimental verification phase module are provided. These extended resources include experimental data processing tools, statistical analysis literature, experimental error analysis data, and alternative experimental schemes. The compatibility relationship between these resources and the core resources is also marked. Based on the core resources of the experimental verification phase after supplementing and extending resources, the supporting services of the experimental verification phase module are configured. The supporting services include experimental resource reservation service, data processing consultation, experimental scheme optimization guidance and experimental result storage service, and the service process and delivery standards are determined. For the results writing stage module, the core resources corresponding to the results writing stage in the dynamic adaptation relationship are retrieved and integrated into the core resources, which include paper writing specifications, reference format guidelines, journal directory for publication of results, literature on writing skills and similar results, and supplementary application scenarios and usage suggestions for the resources. Based on the integrated core resources and supplementary suggestions for the writing stage, extended resources for the writing stage module are added. These extended resources include instructions for using plagiarism detection tools, polishing service guidelines, copyright knowledge materials, and analysis of the publication process, thus supplementing the resource service dimensions. Based on the core resources of the results writing stage after adding extended resources, the supporting services of the results writing stage module are configured. The supporting services include paper polishing service, plagiarism check service, publication consultation service, reference organization service and results archiving guidance, and the service content and related specifications are determined. For the revision and improvement phase module, the core resources corresponding to the revision and improvement phase in the dynamic adaptation relationship are retrieved. The core resources include the peer review comment response guidelines, revision case analysis, academic norm supplementary materials and result optimization literature, and the core value and application methods of the resources are extracted. Based on the core value and application methods of the extracted core resources in the modification and improvement stage, supplementary extended resources for the modification and improvement stage module are provided. These extended resources include peer review comments, recommended modification tools, result display templates, and subsequent research extension materials. The compatibility relationship between these resources and the core resources is also marked. Based on the core resources of the modification and improvement stage after supplementing and extending resources, the supporting services of the modification and improvement stage module are configured. The supporting services include modification guidance and consultation, peer review comment interpretation service, results optimization suggestions and secondary publication consultation, and supplementary service process details. The core resources, extended resources, and supporting services of the experimental verification phase module, the results writing phase module, and the modification and improvement phase module are integrated to form a resource service combination for each resource service module, and the resource connection relationship and service differences between modules are marked.

8. The intelligent recommendation method for library subject services based on big data mining according to claim 4, characterized in that, Based on the module progression sequence, the core resources, extended resources, and supporting services of each resource service module are extracted. The resource service content is then ranked according to its importance in the research process, with core resources and key services given priority. This includes: Analyze each resource service module in the module advancement sequence and extract the core resource list, extended resource list and supporting service list corresponding to each resource service module; The research progress characteristics of users in the subject are retrieved from the subject service data center, the importance evaluation of the research stage corresponding to each resource service module is extracted, and the importance weight of the resource service is determined by combining the frequency of resource use, the degree of demand matching and service-related feedback. Based on the determined importance weights of resource services, the core resource list is sorted, and the core value and applicable scenarios of the resources are marked. Based on the ranking results and importance weights of the core resource list, the extended resource list is ranked according to the strength of its association with the core resources and the importance weights, and supplementary association descriptions are provided. Based on the ranking results of the extended resource list, a supporting service list was compiled, and services closely related to the use of core resources and the progress of research were selected and ranked according to service importance and frequency of use. Based on the ranking results of the supporting service list, supplement the explanation of the basis for ranking resource services, and indicate the source of importance weight and ranking rules for each resource service; The integrated and sorted core resource list, extended resource list, and supporting service list form an ordered resource service combination for each resource service module. The core resources and key services prioritized in the ordered resource service combination are marked, and the ordered resource service combinations of the preceding and following modules are associated. The connection relationship between the core resources of the preceding module and the core resources of the following module is supplemented, and the ordered resource service combination of each resource service module is output and synchronously stored in the recommendation content management module.

9. The intelligent recommendation method for library subject services based on big data mining according to claim 5, characterized in that, For users in disciplines entering a transitional phase, the core resources and services of the modules before and after the research stages are extracted. The common characteristics of resources and services before and after the stages are analyzed, and transitional resources that are suitable for both stages are selected. These transitional resources include cross-disciplinary resources, connecting literature, and comprehensive services, including: Based on the results of the judgment on the turning point, the two research stages before and after the turning point are determined. The core resource list, extended resource list and supporting service list of the modules in these two stages are retrieved to determine the adaptation characteristics and core value of various resource services. The adaptation characteristics and core value of resource services in the two stages are analyzed, and resource types that are adaptable to both stages are extracted. These resource types include interdisciplinary resources, cross-stage connecting literature, general tool materials, and comprehensive service types. Resources that meet common characteristics are retrieved from the library's subject resource pool, and cross-disciplinary resources are screened. These cross-disciplinary resources include literature that integrates the research content of the two stages, cross-disciplinary theoretical materials, and general experimental method manuals. Based on the cross-resources obtained through screening, connecting literature is extracted. The connecting literature includes review articles that summarize the research results of the previous stage and guide the research direction of the next stage, interim research reports, and methodological connecting materials. Based on the extracted connecting literature, the comprehensive service types are screened, the service's applicable scope and content are determined, the correlation strength between the screened transitional resources and the core resources of the preceding and following stages is calculated, and transitional resources with a correlation strength higher than a preset threshold are retained to form a transitional resource list. Based on the established transitional resource list, supplement the detailed information of the resources, which includes the resource name, location in the collection, access channel, content summary, adaptation stage, and a description of the relationship with resources in the preceding and following stages, thus supplementing the resource dimensions; Based on the supplemented resource dimension, the transition resource list is sorted according to its relevance to the preceding and following stages, with transition resources having a balanced relevance being given priority. Based on the sorted transition resource list, the transition resources are associated with the corresponding integrated services to form a combination unit of transition resources and integrated services. Each combination unit corresponds to a type of transition requirement and service support. The transition resource list and the combination unit are then output and synchronously stored in the dynamic recommendation database.

10. A library subject service intelligent recommendation system based on big data mining, characterized in that, The system includes a processor and a memory, the memory being connected to the processor. The memory is used to store programs, instructions, or code, and the processor is used to execute the programs, instructions, or code in the memory to implement the intelligent recommendation method for library subject services based on big data mining as described in any one of claims 1-9.

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