With the rapid development of science and technology, the use of network information technology means more and more widely; network teaching platform has become an important measure to promote the development of education and improve the quality of teaching. The network teaching platform has been widely used and plays a vital role in the construction of teaching informationization and modernization. However, the study of student learning quality evaluation under the network teaching platform has become a huge challenge. In order to solve this problem, this paper takes the teaching of basic Python course as an example, adopts data-driven theory, and combs the teaching process of network teaching platform. Curriculum can be divided into class leading learning stage, stage of studies in class, and after class extension stage and uses the panel method, literature query method, mathematical statistics, performance appraisal, and questionnaire survey method to analyze the factors influencing the quality of learning curriculum implementation, and compared with the traditional classroom teaching, build more indicators of curriculum implementation under the network teaching platform to support students’ learning quality evaluation scheme. The experimental results show that, through the statistics of the evaluation index data, using the analysis method of the KMO coefficient and the KMO test, the three stages of the curriculum implementation of the KMO coefficient and the KMO test prove that the constructed learning quality evaluation scheme has high credibility and good structural validity. Based on class leading learning stage, stage of inquiry learning in the class, after class extension stage, and students’ learning quality correlation analysis, using Pearson correlation test, it is concluded that different index correlations with students’ academic performance prove students’ input and participation, learning ability and innovation ability, self-study ability and students’ learning achievement of great relevance. It has a direct impact on students’ learning quality. The results of this study provide powerful data and theoretical support for educators to carry out network teaching.