Digital public governance refers to the public governance carried out in an era marked by digital technology. It includes both "using technology for governance", that is, using digital technology as a tool to carry out public affairs governance; This also includes "technology governance", which refers to the governance of digital technology itself and its derivative problems, risks, and issues; And to govern the tension between technological logic and public value logic, seeking institutional harmony and order reconstruction. What are the current development directions and trends of digital public governance? What limits are shrinking their extrapolated boundaries? How should we shape the future of digital public governance? In the context of deepening the construction of digital China, answering the above questions is not only a forward-looking and normative reflection on the theory of digital public governance, but also a practical prospect for the future development of digitalization in China and the world. The direction and trend of digital public governance "direction" answers where governance may go in the wave of technology, while "trend" reveals the driving force and inertia behind these trends. The reason why it is necessary to sort out the direction and trends of digital public governance is not to preset a fixed path, but to capture the clues of governance evolution in uncertainty, in order to understand how digital public governance constantly breaks through old boundaries and generates new orders. 1. Knowledge foundation: From empirical induction to data-driven traditional decision-making models, it mainly relies on two types of knowledge sources: empirical judgments accumulated by practical departments in long-term practice, and theoretical induction formed by experts and scholars based on historical data and existing research. In the context of relatively stable governance environment and limited information scale in industrial society, experience and theory can form feasible basis for decision-making and support the generation and adjustment of policies. However, this decision-making logic also has obvious limitations such as information delay, narrow data sources, and lack of complex situation prediction. With the widespread application of digital technologies such as big data and artificial intelligence, the knowledge foundation of public governance has undergone a profound transformation. More and more social operation information can be collected, processed, and analyzed in real time, and data has become an indispensable basic component of public governance. This data-driven cognitive approach is driving the gradual formation of a new knowledge paradigm in public governance - data-driven cognitive and decision-making logic. The Guiding Opinions of the State Council on Strengthening the Construction of Digital Government propose to "fully leverage the role of data as a fundamental resource and innovation engine, improve the scientific level of government decision-making and management service efficiency", which indicates that data-driven has become an important direction for current governance transformation and gradually risen to institutional arrangements. In the process of governance operation, this shift in knowledge base can be summarized as the cognitive chain of "data modeling simulation". On the one hand, the scope and frequency of data collection are constantly expanding, and many aspects of social life are digitally recorded, providing rich and real-time knowledge materials for governance. On the other hand, algorithm modeling is gradually embedded in institutionalized operations. Through structured analysis of massive data, models not only undertake problem identification functions, but also become important cognitive tools for assessing risks and predicting trends. Furthermore, simulation and emulation technology have begun to be used in the policy process, enabling governments to deduce different solutions in a virtual environment and accumulate knowledge and experience before policies are implemented. As a result, the generation of governance knowledge will exhibit new characteristics: shifting from relying on limited experience and static induction to relying more on data-driven dynamic iteration; Shift from focusing on post event summary to paying more attention to pre event prediction. 2. Methodology: From linear reasoning to prediction and experimentation. In existing public governance research, policy analysis and formulation rely heavily on linear reasoning and empirical judgment. This research path mostly has two important assumptions: first, social processes have relative stability, and causal relationships can be grasped through post hoc induction; Secondly, the governance objects have a certain degree of homogeneity, and the "common divisor" and "mean" can represent the overall pattern. But the real world is often complex and ever-changing, and these assumptions face serious challenges: policy outcomes are often accompanied by feedback effects and path dependence, and causal relationships are not single linear; The differentiation of governance targets continues to expand, and the average value conceals the true situation of some groups. The digital age has brought tools and methods to overcome these limitations. Big data analysis can identify patterns and trends in massive amounts of information, compensating for the shortcomings of traditional statistical methods in terms of scale and real-time performance; Machine learning algorithms optimize their prediction and classification capabilities through continuous training, enabling governance to dynamically respond to complex environments; Simulation and emulation technology allows policies to test multiple solutions in a virtual environment in advance, without relying solely on a single point of trial. At the same time, new tools and methods are not single point and mechanical supplements. They simultaneously act on the entire governance process, forming a multidimensional shift: in the time dimension, governance gradually moves away from "post summary" and shifts towards "pre prediction" based on real-time data and predictive models; In terms of logical dimension, policy generation no longer relies on static causal chains, but is constantly revised through dynamic iteration; On the object dimension, policies are no longer solely based on the "majority" as a reference, but respond to differences between groups and regions through finer grained data analysis; In terms of spatial dimension, policy experiments have freed themselves from the limitations of single point trials, and with the help of virtual simulation and digital twins, can be practiced and compared in parallel in multiple scenarios. The overall reshaping of methodology by digital tools makes public governance more forward-looking, flexible, sensitive to differences, and spatially extensible. 3. Subject relationships: The transition from a single center to multi-dimensional subject relationships is a classic topic in public governance discussions. Government centrism is a common feature of traditional public management theory and practice. With the development of society and the diversification of public needs, the governance limitations of a single center structure are gradually emerging. Since the end of the 20th century, the academic understanding of subject relationships has gradually shifted towards diverse interactions, and the focus of governance has also shifted from control to collaboration. In the digital age, the evolution of subject relationships has risen from "diversity" to "multidimensionality". The number and types of entities participating in governance have not only increased dramatically, but their modes of existence and dynamic logic have also shown heterogeneity. The "subject" in the governance system is no longer limited to individual humans and organizations, but extends to technology itself, and forms complex interactions in the physical, social, and digital three-dimensional spaces, forming diverse and multidimensional subject relationships. The emergence of this subject relationship is the result of the mutual reconstruction between digital technology and governance structure, and is manifested in the trend of collaboration, platformization, and networking in practice. For example, in the face of public issues such as climate change and platform economy regulation, a single entity is difficult to cope with alone. The effectiveness of governance depends on the complementary capabilities and resources between different entities, and the governance approach has shifted from departmental segmentation to cross-border collaboration, which relies on platform based technology and institutional conditions. During this process, the functional boundaries of digital platforms are being reshaped, shifting from information channels to intermediaries and operating systems for diverse subject interactions, connecting governments, businesses, society, citizens, and technological entities. Against the backdrop of the multidimensional evolution of public governance subject relationships, the authority of the state has not been dissolved, but has been re embedded into the network through institutional design, data regulation, and algorithmic governance, becoming a key force in maintaining overall coordination and public value. This shift indicates that public governance in the digital age is moving from centralization to decentralization, from control logic to collaborative logic. 4. Ecological governance: From network logic to ecological logic, the traditional hierarchical system maintains order through hierarchy and rules, emphasizing its advantages in resource mobilization and coordinated action. The governance network theory that emerged later attempted to explain the coordination logic under multi-agent interaction, but this assumption has limited applicability in highly dynamic and uncertain contexts. In this regard, the academic community has introduced the metaphor and explanatory framework of "ecology", emphasizing that public governance does not operate in isolation, but is embedded in a whole composed of multiple subjects, institutional arrangements, and the interaction between technology and social environment. The digital age has further amplified the limitations of traditional governance, while giving rise to new coping conditions. The exponential growth in data scale and flow rate means that the governance process can no longer rely on a single center for collection and processing, but must form a distributed response network; The widespread application of digital platforms and algorithms has reshaped the structure of resources and power, with governments, markets, and social entities nested and playing on the platform in new ways; The governance scenarios are constantly expanding, interweaving virtual and physical spaces, making the governance objects and boundaries more fluid; The solution to complex problems requires continuous iteration and real-time adjustment of policies, rather than one-time formulation and implementation. Compared to traditional governance logic, "ecological logic" demonstrates stronger explanatory power and practical adaptability due to its emphasis on system adaptation, continuous learning, and trust mechanisms. The transformation from network logic to ecological logic does not negate hierarchy or networks, but rather places them in a larger ecosystem for understanding: the resilience and effectiveness of governance depend more on the quality of multi-agent interaction, as well as whether trust and learning mechanisms can be institutionalized, contextualized, and continuously operated, thereby supporting the sustained generation of publicness. From a trend perspective, ecological governance is gradually evolving from localized and problem oriented collaborative practices to institutionalized systematic logic. The future of digital public governance may be more manifested as an open system that spans across organizations, fields, and levels, emphasizing adaptive adjustment and resilience. This means that ecological governance is not only a passive response to complexity, but also an active framework for reshaping governance order. At present, digital technology seems to provide unprecedented possibilities for public governance, but the possibilities do not equal infinity. Digital public governance has its inherent limitations, which are neither purely technical defects nor individual institutional barriers, but stem from the structural boundaries revealed in the interaction between technology and society. 1. The limitations of technology itself are related to public interests, and its targets are complex and ever-changing social systems and active individuals. When digital technology is introduced into the field of public governance, its potential technological flaws may touch upon citizens' basic rights, social equity, and even national security, and may even evolve into unbearable social risks and trust crises. Firstly, the authenticity limit constituted by data distortion. Transforming complex social reality into processable data inevitably comes with information loss and selection bias. Therefore, any governance object that is characterized by data to enter the governance process will inevitably bring measurement errors, selection biases, and contextual loss. Secondly, the stability challenge brought about by algorithmic bias. The social inequality factors contained in the training data of algorithms will be learned and solidified by algorithms, thus forming technical discrimination. At the same time, digital systems operate in highly non-stationary social environments. With changes in time, context, and institutions, models and algorithms often experience "distribution drift," leading to instability in their predictions and judgments, thereby affecting the fairness and reliability of governance decisions. Thirdly, the controllability dilemma caused by the amplification of system risks. The scale, connectivity, and automation speed of technology enable individual errors to instantly evolve into systemic risks. For example, if the circuit breaker mechanism in the financial market fails due to algorithm errors, it may trigger a chain reaction and cause huge losses. Fourthly, the ambiguity of responsibility attribution weakens accountability. Cross level "cloud edge end" architecture, closed source model, and cross-border