Teaching Quality of Ideological and Political Education in Colleges Based on Deep Learning

Introduction

As one of the important tools to ensure and promote the continuous improvement of higher education quality, higher education quality evaluation is to conduct in-depth research, effective management and big data analysis of the large amount of raw data received by colleges in the teaching process, which can be used to evaluate TQ. And the introduction of the corresponding improvement measures to provide decision-making support (Lin & Chen, 2020; Erdem et al., 2020).

With the development of computer and information technology, many scholars use mathematical models to directly establish educational quality evaluation systems, especially fuzzy comprehensive evaluation methods, cluster analysis, analytic hierarchy, and gray systems. Fuzzy comprehensive evaluation (FCE) is a synthesized appraisal approach based on fuzzified logic, as well as the discussed from the perspective is realistic and dependable, making it extensively utilized in the petrochemical, architecture, and other areas. The AHP's three primary roles are (1) organizing sophistication, (2) evaluating on a quantitative approach, and (3) synthesizing, and these are the simplest theories to interpret. The grey systems approach is a novel paradigm for studying issues with images obtained and little knowledge. As a result, the functional behaviors of organizations and underlying rules of development may be accurately characterized and observed. Some scholars use the fuzzy set 2-type multi-level model to evaluate TQ. Type-2 fuzzy logic is an extension of standard inductive inference (type-1) in the perspective that ambiguity is inherent not just in the formulation of the attribute values and also in the input parameters. Some scholars apply the analytic hierarchy process and neural network to the education quality evaluation model (Imran, (2019); Zhang, (2020)). Some researchers combine the fuzzy analytic hierarchy process with the fuzzy overall evaluation method to provide a quality evaluation model for higher education managers and another tool for improving TQ (Peng, (2020); Li, (2021)). Fuzzy Analytic Hierarchy Process (FAHP) is a fuzzy logic-based Analytic Hierarchy Process (AHP) approach. The fuzzy AHP approach is comparable to the Evaluation criteria. The Fuzzy AHP approach simply converts the AHP scaling into a fuzzy triangle level that may be accessible first. Some researchers also use the TOPSIS method of multi-functional decision-making to evaluate TQ (Zureck, 2021; Long & Zhao, 2020). Among the analytical models of multi-criteria strategic planning is TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). This is an approach that may be used in a variety of situations and is based on an appropriate statistical concept. Moreover, it is a very realistic strategy that relies on specialized software. The key premise is that the selected option must be closest to the fitness value and furthest away from the negative optimization task. Farthest away from the negative effective solution. Deciding based on several factors. In short, the advantage of the above method is that it considers various evaluation factors and combines the experience and knowledge of experts. The disadvantage is that the evaluation process without considering the evaluation indicators is random and subjective, and the non-linear relationship of the evaluation results is very subjective, so it does not really reflect TQ. In recent years, many researchers have used neural network technology to evaluate educational quality evaluation models (Fernandez et al., 2013). Neural networks are used in the field of prediction. Two scholars use programs to simulate neural networks, learn knowledge of data samples, and predict nonlinear data in a computer environment (Du, 2020; Mbise, 2021). Some scholars apply the BP neural network algorithm to evaluate TQ. It's a common method for teaching convolutional neural networks. This approach is useful for considering all the factors of a damaged system with regard to all of the platform's values. In addition, a researcher used the BP neural network method to establish a model of the education quality evaluation system in colleges, quantifying the educational achievements as the output, taking the meaning of the education evaluation indicators of specific data as the input, and the teaching effect as the output (Niet et al., 2016; Ahmad et al., 2021; Tang et al., 2020). Using MATLAB for empirical research, the application of this method in education quality evaluation can surpass the subjectivity of expert evaluation, and the results are relatively accurate. This method has good application value.