Abstract
This paper proposes the functional model and application service implementation process of the education cloud platform application service architecture. The entire cloud application service architecture mainly includes four parts: cloud service management, cloud application service rapid creation and deployment, dynamic process configuration, and unified identity authentication. Based on the basic theory of workflow, the process status and business services of cloud application services are discussed. The BP neural network weight optimization model based on the improved quantum evolution method is studied, and a method that combines the improved quantum evolution algorithm (IQEA) and the BP algorithm to complete the back propagation neural network training is proposed, that is, the IQEA-BP algorithm. Firstly, the traditional quantum evolution algorithm is improved, and then, the improved quantum evolution algorithm is used to optimize the network weights as a whole to overcome the shortcomings of the BP algorithm that is easy to fall into the local optimum; then, we use the BP algorithm to find the better weight as the initial value to improve the training and prediction accuracy of the network. In order to enrich the school education quality evaluation system, this article adds soft indicators that can reflect school education performance on the basis of the existing “National Education Inspection Team” indicators and uses analytical methods to prove the effectiveness and feasibility of the new evaluation indicators. The X1-X10 index data is selected as the evaluation index of the school education quality evaluation system in this paper. Testing the performance of the BP neural network, the accuracy rate of the school education quality evaluation is 93.3%, the average absolute error is 0.067, and the accuracy and recall rate of the test set grade gradient of 0, 1, 2, 3, 5, 6, and 8 are all 93%, indicating that the IQEA-BP neural network algorithm has a good effect on the evaluation of school education quality.
Highlights
The classroom teaching process and the supervision of the teaching quality in the classroom are an effective method to ensure the quality of classroom teaching [1]
This paper introduces the method of initial weight optimization of a neural network, analyzes and discusses the advantages and disadvantages of various methods, and proposes a combination of improved quantum evolution algorithm (IQEA) and BP algorithm to complete the back propagation neural network
The algorithm first improves the traditional quantum evolution algorithm and uses the improved quantum evolution algorithm to optimize the network weights as a whole, to overcome the shortcomings of the BP algorithm of falling into the local optimum
Summary
The classroom teaching process and the supervision of the teaching quality in the classroom are an effective method to ensure the quality of classroom teaching [1]. Teaching evaluation is one of the most commonly used methods in teaching supervision activities It is to objectively inspect and judge both the teacher’s teaching process in the classroom and the student’s learning quality [3]. Through the supervision and assessment of students’ learning quality, the results of the implementation can be fed back to teachers, so that they can improve or strengthen certain aspects of teaching, so as to better and more timely ensure that teachers complete teaching tasks within the corresponding teaching timeliness. In today’s society where informatization continues to develop, information technology can be used to establish a more complete teaching evaluation system [5] Through this system, teachers’ teaching conditions can be monitored in real time, and teachers can understand how they are teaching in a shorter period of time. We use the BP neural network evaluation model to comprehensively evaluate the quality of school education and explore the important and difficult points of the BP neural network evaluation model based on the compulsory education balance index, including the network structure design of the BP neural network and the determination of the number of neurons in the hidden layer
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