2.1. Government Big Data Classification Organization

The external big data generated within the government and collected by the government for work development and management service needs are important to the government’s decision. This definition mainly defines government big data from the attribute of big data resources and the dimension of resources [11]. Therefore, it can also be called government big data resources. Accordingly, industry departments pay more attention to data disaster recovery. Still, due to the lack of top-level design and overall planning, there are some phenomena, such as follow-up construction and repeated construction in some local data disaster recovery centers. In addition, the structure of data disaster recovery requires high capital and technology, and the one-time investment is huge. Therefore, the classification of government data should be based on the natural attributes of data, such as scientificity, stability, practicability, and scalability.

Government big data classification has diversity according to different methods. This is because government big data classification must meet the needs of users. Still, in fact, the needs of users are diverse, so a single classification method is bound to be difficult to meet the personalized needs of users. In addition, the classification of government big data is uncertain. For example, if divided by subject, these data may not have a suitable subject, or they belong to one subject and conform to another; if it is divided according to institutions, these data sources may not be just a single department. If the division is unreasonable, it will affect the sharing and utilization of data. In addition, some data resources can be distinguished only from the perspective of the level and degree, so we should pay attention to the flexibility of classification.

The flow of data flow in the process of government big data classification and organization roughly presents three structures. The first is the central radiation type, where the data collection, processing, retrieval, and transmission are concentrated in the organization’s data center. The data center provides and publishes data according to the data request required by each department. The second is horizontal, where there is spontaneous regular or irregular organizational data exchange within the organization. The third type is crisscross. Each department manages part of its data independently and establishes interdepartmental links. Finally, the data will be concentrated in the data center. The flow structures of these three data streams are realized under organizational power. The differences in the organizational power of these three structures are analyzed from the perspective of a super network.

2.2. Classification and Organization System of Government Open Data Resources from the Perspective of Hypernetwork

From the users’ perspective, the problems existing in the data resource classification of China’s government open data websites can be summarized as the following two points: first, the depth and accuracy of open data resource classification are not enough; second, the classification method is single. First, the depth is not enough. According to the observation results of the data disclosure directory of domestic government data open websites, the categories are only level 1 and level 2 in-depth. Furthermore, some have only level 1 directory without level 2 and level 3 directories. Therefore, in terms of resource coverage, the resources currently provided are difficult to meet the main needs of the public. Secondly, the accuracy is not enough. For example, the first-level category covers major fields such as politics, economy, culture, science and technology, and ecological environment. However, the content resources listed under the second-level category are not complete. The first-level directory and content do not have a strong division or matching degree. Furthermore, there is a problem with the lower-level category being next to the upper-level category, which causes issues with data retrieval and low precision. In addition, the metadata description is not detailed enough, and most of them only contain some simple data attributes. These attributes seriously restrict the open serviceability of government big data resources and cannot effectively meet the public’s demand for value-added development and reuse of data resources.

Government managers coordinate the classification of government open data resources, catalog classification experts, and data open resource users. Big data resources are transformed from chaos to order through the process of categorizing resources using various categorization dimensions, examining the relationships between data, filtering, sorting, and adding labels to data. Its goal is to create a sensitive, organized, natural, understandable, concise, lively, harmonic, and unified classification system structure. With such a system, consumers may easily find, browse, and use government open data resources.

3.1. Key Issues of Traditional Government Big Data Management

This paper roughly divides government information sharing into three stages and summarizes its characteristics. In the first stage, the focus of China’s government information resource sharing is vertical information sharing, which is characterized by information sharing among fields, levels, and specialties. Its application is mainly to facilitate government management. In the second stage, China’s government information resource sharing began to emphasize horizontal information sharing, but it has an obvious trend of data center, which is characterized by centralized cross-domain, cross-department, and cross-platform information sharing. Its application is to better help government decision-making, which is decision-making sharing. In the third stage, the focus of sharing is still horizontal sharing, but more attention is paid to distributed sharing. Its feature is centralized distributed, that is, to ensure the depth and breadth of sharing, its application is to better help government decision-making and public services, the need of building an intelligent government, and service decision-making sharing.

In specific practice, the government information resource sharing mode can be generally divided into the following two types: one is the point-to-point (P2P) information sharing mode; the other is the information sharing and exchange mode of the information resource management center, as shown in Table 1. These two modes have different characteristics, advantages, and disadvantages and also have their own applicable scenarios.

The data disaster recovery center aims at “soft” disasters such as network hacker attacks, computer viruses, illegal intrusion, human operation errors, or malicious destruction, as well as “hard” disasters such as natural disasters such as fire and flood or hardware failures. It is an emergency remedy for data recovery and business continuity caused by network paralysis, data loss, and business interruption. It is an important infrastructure and service application center in the national information security system. The value of data is higher and higher. The loss of data will bring great harm to social and economic development and public life. Therefore, various industry departments pay more and more attention to data disaster recovery, but due to the lack of top-level design and overall planning, there are some phenomena such as follow-up construction and repeated construction in some local data disaster recovery centers. The construction of data disaster recovery requires high capital and technology, and the one-time investment is huge. The self-built model will consume a lot of energy for the organization, and the outsourcing model also has the risks of the system being out of control, key data loss, and so on. Local disaster recovery cannot cope with regional disaster risks. Remote disaster recovery can deal with a wide range of disaster risks, but the investment cost is high.

3.2. Government Big Data Super Network Management Based on Blockchain

Blockchain is a public ledger based on computer programs. It is a widely participated distributed accounting method. Its essence is to jointly maintain the reliability of the database through decentralization and distrust. It is a tool to help people cooperate in complex and multifields that are not familiar with each other. The main logical entities involved in the blockchain system can be abstracted into department business centers, alliance nodes, other nodes, department data centers, and information databases. Among them, the alliance node is mainly composed of the data centers of civil affairs, health, education, public security, finance, and other government administrative departments or service institutions. Each data center is the information generated and managed by each business field. Alliance nodes include two functions: information transmission and information processing. Each alliance node can provide point-to-point and point-to-multipoint information routing, information transmission, and other functions to store and share data through the consensus mechanism and smart contract function automatically operated by the blockchain system. The imported information is encrypted by the cryptographic algorithm and cannot be tampered with. All alliance nodes jointly maintain and supervise the information. Only when the information of this node is consistent with that of other nodes, that is, to ensure that the information is reliable and nonrepetitive, it can be imported into the blockchain storage system and stored and backed up in the information database. Other nodes are mainly ordinary users such as the public, enterprises, institutions, and nonprofit organizations. After being authorized by the government administrative department, they can search the basic information of individuals in the database and cooperate with the entry of node information.

The method of government information collaborative sharing and data disaster recovery based on blockchain draws on the advantages of traditional data sharing and disaster recovery methods. It can not only realize point-to-point information sharing and exchange among multiple departments but also realize decentralization and distrust. At the same time, it can be backed up, shared, and used many times, and information traceability management, which greatly increases the scope and efficiency of information sharing. Generally speaking, it has the core characteristics and advantages of good credibility, high security, strong transparency, expandability, economy, real-time, and so on.

4.1. Government Big Data as a Service Approach from the Perspective of Hypernetwork

The definition given in this paper is that government big data as a service are a process of providing services on demand based on the data as a service model and supported by modern information and communication technology, using the perspective of government big data super network and taking users as the center, including data discovery, data fusion, and data interaction. Government big data as a service are an infrastructure and a necessary condition for the intelligent construction of the government, with obvious hierarchy. The conceptual model of government big data as a service is shown in Figure 1, which is mainly composed of resources, technology, services, and environment.

4.2. Government Big Data for Public Service Is the Service Guarantee System

The government’s big data capability evolved from the big data capability, emerged from the application of big data technology to government work, and the government’s ability to obtain, process, and apply data to ensure scientific decision-making and efficient operation. Specifically, the connotation of government big data capability includes three aspects: data acquisition capability, data processing capability, and data application capability. The development of a data resource catalog classification system and the capacity of the government’s big data centers for collaborative sharing and data disaster recovery should both benefit from the expansion of big data services. Strengthening the supervision and guarantee capacity of data security, data privacy, and data fraud, improve the development and reuse capacity of data and convert more dormant data into available data.

The value of data is mainly reflected in the matching degree between data association, sharing, and demand. The operation of data association and sharing can be understood as the process of the data resource configuration. Therefore, according to the degree of resource development and actual demand, the utilization of data value can be divided into four types: inefficient, deficient, wasteful, and efficient. In the era of big data, data are widely dispersed in different carriers. Therefore, it is necessary to change the data from disorder to order to form a complex but rule-based data cyberspace. The whole network organization is to connect each node on the network, realize the self-flow of data on the whole network, effectively schedule each node on the network, coordinate the connection between nodes, and completely solve the problem of data island.

The government’s public service must thoroughly implement the purpose of serving the people wholeheartedly, and change the negative impression that it was difficult for the public to enter, do things, and look ugly in the past. The implementation of government policies and the promotion of services need the cooperation of the general public. Therefore, government services must also establish a good reputation, form network communication, and unite the hearts of the people. The capability and manner of public service of a local government department in particular frequently influence the regional economy and social growth. It can effectively entice investment, talent, and other things. As a result, the management division of the public sector of government needs to learn from the practice of keeping up relationships between businesses and other organizations as well as users. Organizations and users place a high value on sustaining users, providing excellent user service, encouraging strong user commitment, and maintaining active user communication.