As a foundational system, standardization underpins the high-quality development of digital governments. This study reviews China’s e-government and digital government standard systems, analyzes the quantity and structural characteristics of relevant standards, and identifies key existing challenges. It emphasizes promoting the system’s transformation toward systematic construction, forward-looking guidance, and substantive implementation through strengthened systematic top-level design, innovative standard development mechanisms, and improved implementation guarantees. Integrating the “five-element system” of digital government construction, a “1+5” standard system framework is proposed to support its high-quality development.
Based on the policy practice background of “one-stop government services”, this paper systematically explains its conceptual connotation, clarifies the classification standards, sorting principles, and practical paths of main points, and proposes a path to expand and enhance efficiency oriented towards “one type of matter”. The study believes that the essence of “one-stop government services” is to break down departmental barriers and information silos through business process reengineering, institutional innovation, and data empowerment, achieving a shift in government services from the “government supply side” to the “public demand side”. This concept requires not only focusing on high-frequency and rigid demand matters with precise efforts, but also emphasizes online and offline integration and departmental coordination and collaboration, ultimately building a full-life cycle and full-chain coverage government service system to provide enterprises and the public with convenient, efficient, and high-quality government service experiences.
Digital intelligence empowerment, with officials taking the lead and quality serving as the foundation, has evolved significantly in conceptual framework. The notion of digital intelligence literacy has progressed from “information literacy” to “digital literacy”, and further matured into the current concept of “digital intelligence literacy”. At present, this concept emphasizes the innovative integration of big data and artificial intelligence technologies. In alignment with competency theory, a multi-level, progressive structural system has been established, encompassing digital intelligence awareness, digital intelligence skills, and digital intelligence capabilities—corresponding respectively to the practical dimensions of “willingness to act” “ability to perform” and “proficiency in execution”. To address existing challenges in capacity development—such as reluctance to adopt, inability to apply, and ineffective utilization—targeted cultivation strategies including ideological refinement, professional training, and practical exercises are systematically applied to enhance the effectiveness of talent development initiatives.
With the deepening of digital government construction, government data has become a core element driving the modernization of national governance capabilities. Given that the current government data sharing still faces numerous challenges such as “data silos” and semantic heterogeneity, which seriously restrict the effective release of data value, knowledge graph technology is introduced to propose a new model of government data sharing based on graph information penetration to solve these problems. This model aims to break down data barriers and achieve deep correlation and semantic interoperability of government data by constructing a multi-level information penetration system covering field-level, system-level, and business-level. The “three-dimensional integrated” mechanism system based on graph information penetration covers three dimensions: graph construction and evolution, penetrating services and collaboration, and graph based dynamic security and auditing. It achieves a paradigm shift from “data management” to “knowledge operation” and ensures the smooth implementation of the new model.
Against the backdrop of global competition and rapid advancement in artificial intelligence (AI) technologies and applications, generative AI (GenAI) is becoming increasingly dependent on high-quality government data, while simultaneously disrupting the established boundaries of traditional open government data (OGD) practices. Drawing on literature review and logical induction, this paper systematically examines the interactions between GenAI and OGD across the stages of data acquisition, model training, and application. It reveals how GenAI drives a reconfiguration of boundaries in three key dimensions of OGD: the scope of data subjects, data quality governance, and platform infrastructure. By unpacking these dynamics, the study elucidates the profound challenges GenAI poses to OGD initiatives and offers theoretical insights and policy implications for navigating the evolving landscape of government data openness in the age of generative AI.
Optimizing government service processes is a critical pathway to advancing the digital transformation of government services, while scientifically guided methodologies and robust technological tools serve as important guarantees for achieving such optimization. In response to the new requirements for government service processes against the backdrop of digital transformation, this study examines the applicability of the “Eliminate-Simplify-Integrate-Automate (ESIA)” process optimization method and its specific application in optimizing government service processes. Furthermore, it proposes an optimization framework for government service processes that integrates Robotic Process Automation and Artificial Intelligence technologies. The aim is to provide theoretical support and practical guidance for optimizing government service processes and facilitating the digital transformation of government services.
Addressing the limitations of traditional methods in terms of efficiency, accuracy, and professionalism, Large Language Models (LLMs) facilitate an intelligent transformation through capabilities in natural language processing, data analysis, and decision support. This paper analyzes the role of LLMs in automating workflows, enhancing risk identification, supporting scientific decision-making, and structuring expert knowledge. It confirms their effectiveness in improving evaluation efficiency, optimizing resource allocation, and mitigating risks. Meanwhile, challenges such as data security, model interpretability, and ethical governance remain. The paper concludes that LLMs will significantly advance the intelligent and standardized development of government investment evaluation, supporting the broader goals of smart governance.
As users’ expectations for telecommunications services continue to rise, a key challenge for the industry is to strengthen its complaint resolution capabilities and consistently meet the people’s growing demand for high-quality service. The application of AI technology can significantly enhance the quality of user complaint services, improve the efficiency of complaint handling, and reshape the complaint service model. By reviewing the main application scenarios of AI in the telecommunications complaint domain, the key challenges currently faced in its implementation, and its future trends, the study aims to offer insights into how technological means can be leveraged to improve telecommunications user service capabilities.
With the continuous advancement of Artificial Intelligence (AI)-based recognition technologies and the deepening digital transformation of government services, AI recognition in public-sector applications is expected to experience substantial growth. Contract data entry and invoice recognition constitute key components of the contract execution and payment phases within the government procurement management workflow, serving as crucial steps to ensure compliance and operational efficiency. This paper proposes an intelligent form automation tool tailored for government service scenarios. The tool integrates optical character recognition, semantic fuzzy matching, and vision-driven robotic process automation technologies, achieving full-process automation—including field extraction, semantic error correction, and cross-system data entry—through multimodal collaboration. Experimental results demonstrate that the tool attains an average overall accuracy of 99.2% across tasks such as contract field parsing, invoice information extraction, and result entry, significantly reducing manual data-entry costs and improving the efficiency of administrative workflows. The findings provide a scalable and practical technical pathway for deploying lightweight AI solutions in grassroots government contexts.
The online intermediary service supermarket represents a pioneering local institutional innovation aimed at addressing administrative dependence and market distortion in China’s intermediary service sector. Using Huizhou City in Guangdong Province as a case study, this paper develops an analytical framework of “institution—platform—rule execution” to examine how the reform evolved from local experimentation to provincial institutionalization and cross-provincial diffusion. The study finds that Huizhou reconstructed the governance framework through standardized service lists, open market entry, and credit-based supervision, forming a rule system that can be operationalized and quantified. By embedding institutional logic into platform architectures, the reform enabled algorithmic rule enforcement, full-process traceability, and scalable institutional replication, exemplifying an “institutionally embedded informatization” model. Furthermore, the diffusion of this model follows a “policy template + platform system” mechanism, highlighting the technological and tool-based characteristics of institutional propagation in the digital government era.
In order to solve the efficiency dilemma of digital empowerment rural governance and realize the effective adaptation of technical tools to local situations, this study takes the digital integral system as the starting point, based on the standardized governance theory, and takes Z village in G city as an example to construct an analysis framework of “index quantification-rule construction-value goal” to analyze the root of its dilemma. It is found that the practice of digital integral system in Z village highlights three tensions: the conflict between clear governance standards and local vague affairs, the adaptation dilemma between rigid rule constraints and local practice flexibility, and the deviation of technocracy from the value order of the life world. Its deep root lies in the internal contradiction between the standardization of technical rationality and the elasticity of local knowledge and demand. Therefore, we should deconstruct the shackles of absolute standardization of technical rationality and explore a more inclusive and simplified governance model to effectively empower the modernization of rural governance.
While the digital transformation of grassroots governance is accelerating, the digital divide faced by vulnerable groups is becoming increasingly prominent. This study selects three distinct scenarios—aging communities, county-level rural areas, and mixed urban neighborhoods—and analyzes digital divide bridging strategies in four dimensions: access, use, capability, and effectiveness. The analysis reveals trends such as online-offline integration, enhanced digital awareness, and multi-stakeholder collaboration. Based on these findings, the study proposes a structured, multi-pronged strategy framework to provide practical guidance for promoting inclusive development in the digital transformation of grassroots governance.
As a key hub connecting technological innovation and user demands, Artificial Intelligence (AI) terminals are reshaping the global industrial landscape. By systematically reviewing the current development status and trends of the AI terminal industry, this paper focuses on analyzing the technological evolution paths and commercial applications of typical products such as AI phone, AI PC, and AI in-vehicle terminal, and dissects the four-in-one collaborative ecosystem and the new traffic entry paradigm of the industry. To promote the high-quality development of the AI terminal industry, this paper proposes suggestions such as strengthening ecological collaboration, intensifying technological research and development, and improving the standard system.