Construction of Teaching Service Quality Evaluation Index System under the Digital Background

In order to solve the problems of low system integrity, poor closeness and reliability of evaluation indicators in traditional methods, a construction method of teaching service quality evaluation index system under the digital background is proposed. Complete the selection of evaluation indicator data through the ripple effect model. By calculating the Min's distance to measure the similarity of evaluation index data and removing data with high similarity, principal component analysis (PCA) is used to normalise and reduce the dimensionality of the data. Using extreme learning machine algorithm to classify and process evaluation indicators, and achieving research on the construction of teaching service quality evaluation indicator system. The case analysis results show that when the number of indicators is 1000, the completeness of the indicator system of the proposed method is 98%, the closeness is closer to 1, and the maximum reliability is 97%, which has the characteristics of high feasibility.