Abstract
With the development of information technology and the deepening of government system reform, e-government has become one of the key points of government construction. However, both in developing countries and developed countries, there is a common phenomenon that the success rate of e-government projects is not high. Although various e-government evaluation index systems have been proposed in both theoretical research and application aspects, which provide strong support for the development of e-government, these index systems still have limitations. This paper comprehensively applies BP neural network and adaptive matching tracking, and constructs an e-government performance evaluation model based on adaptive matching neural network. We conduct simulation experiments on the constructed model to verify the accuracy and rationality of the model evaluation. It is feasible to introduce the Balanced Scorecard to construct the E-government performance evaluation index system. The constructed index system has strong objectivity, operability, comprehensiveness, guidance and sustainability, and can be applied to the practice of e-government performance evaluation. It is feasible to use the adaptive matching tracking neural network model to evaluate the performance of e-government. The model has the ability to self-learn the evaluation samples, and can grasp the mapping relationship between the evaluation indicators and the evaluation results, so as to imitate the evaluation of experts, and overcome the evaluation randomness and subjective uncertainty that are difficult to get rid of in traditional manual evaluation. In order to better evaluate the operation status of the e-government system and its adaptability to future needs, and find out its existing problems, this paper fully considers the interaction process between the government and enterprises, and establishes a corresponding responsive government. The application of adaptive matching tracking neural network model for e-government performance evaluation has better optimization results than the original BP neural network model. The specific performance is that the convergence speed is fast, the training will not fall into a local minimum, and the error of the evaluation results is small.
Highlights
With the development of information technology and the deepening of government system reform, e-government has become one of the key points of government construction
E-government is an open social public service system, covering all aspects ranging from the country to small enterprises and the public, as well as civil servants. e evaluation subjects introduced in this article are mainly individuals, groups or organizations that receive the main services of e-government or can influence the development of e-government, such as national departments, government agencies at all levels, enterprises and institutions, the public, and civil servants
In order to reduce the pressure on transmission and storage equipment caused by excessive signal acquisition, while sparse and under-sampling compression is performed on the target signal, the acquired signal is adaptively blockcompressed according to the composite period, which reduces the storage space of the measurement matrix and the sparse matrix
Summary
With the development of information technology and the deepening of government system reform, e-government has become one of the key points of government construction. E application of adaptive matching tracking neural network model for e-government performance evaluation has better optimization results than the original BP neural network model. Mathematical Problems in Engineering favored by governments around the world, and government departments are constantly reforming in the direction of providing quality services to citizens [4]. E development of e-government is constantly adapting to new changes in Internet information technology, and making full use of emerging technologies to create a favorable environment for government services can provide a favorable external guarantee for the sustainable development of service-oriented government construction [8]. Based on the conclusions and based on theories and knowledge related to public management and e-government service management, corresponding suggestions can be made for relevant departments [10] It is of guidance and practical significance for the future to continue to build more high-quality e-government services to accumulate experience and knowledge
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