近年来，信息技术的快速发展不仅深刻改变了企业的生产经营模式，也为政府优化惠企政策信息服务提供了坚实的技术支撑和发展机遇。各级政府通过构建综合服务平台、打造多媒体宣传矩阵等方式，为企业提供了多元化的线上政策信息获取渠道。然而，面对海量的政策信息和多样化的企业需求，如何提供精准的惠企政策信息服务成为亟待解决的问题。为此，政府必须精细服务模式，紧密结合企业特征和需求，实现惠企政策信息的智能推送服务。

尽管当前各级政府已陆续在企业一站式政务服务平台上线政策推送功能，并且已有领域学者围绕该话题开展了深入研究，但在实际应用中仍然面临资格匹配精准度不高、政策信息语义关联不足等挑战。其关键原因之一在于缺乏一套科学、系统的惠企政策信息组织模型。在此背景下，针对惠企政策推送的应用场景，本研究提出了基于本体的惠企政策信息组织框架。该框架旨在构建政策信息的精细化特征表示，为后续的推送匹配算法提供坚实的数据基础，进而提升政策与企业的推送服务水平。

本研究遵循“本体模型构建-应用场景和流程架构分析-实证研究”的逻辑脉络，对惠企政策本体构建展开了探索研究。

首先，针对惠企政策推送的应用需求，本研究基于政策网络理论，构建了一个结构清晰的惠企政策本体模型。与已有政策信息组织模型相比，本模型由“政策基本框架描述层”和“目标群体描述层”两大核心部分构成，并在“目标群体描述层”中引入企业画像标签体系，使模型更加贴近惠企政策推送的实际应用。这一设计使得本体模型能够更有效地组织政策信息，为后续提升政策推送的针对性和有效性提供有力支持。

接着，基于已构建的惠企政策本体，本研究进一步探讨了三种典型的基于本体的推送场景：基于资格条件的推送、基于协同过滤的推送以及基于定制兴趣的推送。针对这些场景，分析了实际应用中的需求与实现流程。在此基础上，提出了基于惠企政策本体的推送应用的具体实现流程与系统架构，旨在为惠企政策的精准推送提供系统的流程参考。

最后，本研究进行了基于惠企政策本体的实例填充和推送实验。通过数据准备、信息抽取、形式化表示及数据存储等步骤，完成了惠企政策的实例化。在完成实例化后，基于实例化的数据，本研究以资格条件推送为例，进行了实验验证，并对实验结果进行了分析和评估，证实了本体模型在实际匹配计算中的有效性。基于实验结果，研究采用定性分析和基于OntoQA定量评估方法，从本体模型维度和实例维度对构建的惠企政策本体模型进行了全面评估。评估结果显示，该模型类别丰富、属性明确、结构合理，能够很好地适应惠企政策信息的结构化组织需求。

总体而言，本研究提出的惠企政策本体模型具有清晰的结构和丰富的内涵，这使得它能够全面、精准地描述和解析惠企政策的各个方面。同时，该模型能够有效支持政策信息的组织和政策推送的应用。展望未来，研究将深入探索本体模型的智能构建技术和实例自动化抽取方法，并努力构建惠企政策服务平台，旨在将研究成果转化为实际应用，为更广泛的政策推送和企业需求提供服务。

In recent years, the rapid development of information technology has not only transformed the production and operation models of enterprises but also provided solid technical support and development opportunities for governments to optimize their information services for preferential enterprise policies. Governments have provided enterprises with diversified online channels to access policy information by building integrated service platforms and creating multimedia promotion matrices. However, faced with the vast amount of policy information and diverse enterprise needs, how to provide precise information services for preferential enterprise policies has become an urgent issue. To this end, the government must refine its service model, closely integrate with enterprise characteristics and needs, and achieve intelligent recommendation services for benefit enterprise policy information.

Although government departments have introduced policy recommendation functions on enterprise one-stop government service platforms, and scholars in the field have researched this topic, challenges like low accuracy in qualification matching and insufficient semantic relevance of policy information persist in practical applications. One of the key reasons is the lack of a scientific and systematic specification for the policy information organization model. Against this backdrop, this study proposes an ontology-based framework for organizing benefit enterprise policy information, targeting the application scenario of benefit enterprise policy recommendation.

This study explores the construction of the ontology for preferential enterprise policies following the logical sequence of "ontology model construction - analysis of application scenarios and process architectures - empirical research".

First, based on the application requirements of benefit enterprise policy recommendation, this study constructs a clearly structured ontology model for preferential enterprise policies using policy network theory. Compared with existing policy information organization models, this model consists of two core parts: the "policy basic framework description layer" and the "target group description layer". The introduction of an enterprise portrait tagging system in the "target group description layer" makes the model more relevant to the practical application of benefit enterprise policy recommendation. This design enables the ontology model to organize policy information more effectively, providing strong support for subsequent improvements in the pertinence and effectiveness of policy recommendation.

Next, based on the constructed ontology for preferential enterprise policies, this study further explores three typical ontology-based recommendation scenarios: recommendation based on qualification conditions, recommendation based on collaborative filtering, and recommendation based on customized interests. The study analyzes the needs and implementation processes in practical applications for these scenarios. On this basis, it proposes specific implementation processes and system architectures for the recommendation application based on the ontology of preferential enterprise policies, aiming to provide systematic process references for precise recommendation of preferential enterprise policies.

Finally, this study conducted an experiment on instance filling and pushing based on the ontology of preferential enterprise policies. Through data preparation, information extraction, formal representation, data storage, and other steps, the instantiation of preferential enterprise policies was completed. After instantiation, based on the instantiated data, this study conducted experimental verification taking qualification condition pushing as an example, and analyzed and evaluated the experimental results, confirming the effectiveness of the ontology model in practical matching calculations. Based on the experimental results, the study adopted qualitative analysis and OntoQA-based quantitative evaluation methods to comprehensively evaluate the constructed ontology model of preferential enterprise policies from the ontology model dimension and the instance dimension. The evaluation results showed that the model is rich in categories, has clear attributes, and has a reasonable structure, which can well adapt to the structured organization needs of preferential enterprise policy information.

Overall, the proposed enterprise-friendly policy ontology model in this study possesses a clear structure and rich connotation, which enables it to comprehensively and accurately describe and analyze all aspects of enterprise-friendly policies. Meanwhile, the model can effectively support the organization of policy information and the application of policy recommendation. Looking ahead, the research will delve into the intelligent construction technology and automated extraction methods for ontology models and strive to build an enterprise-friendly policy service platform, aiming to transform research results into practical applications to serve a wider range of policy recommendation and enterprise needs.