Research on the Quality Evaluation Model of Remote Network Teaching Using Computer Big Data Technology

This study is rooted in the fertile soil of computer massive data technology and conceives an innovative distance education quality evaluation system. This system integrates the sophistication of autoencoders and achieves in-depth analysis of teaching materials through feature extraction, dimensionality reduction and other means. In the initial step, autoencoders are used to implement data encoding and decoding, explore deep characteristics and implement dimensionality simplification; then, evaluation standards are constructed based on these characteristics to achieve a comprehensive evaluation of teaching quality. In the model simulation stage, many actual data sets are imported to comprehensively test the model's effectiveness. The simulation results show that the developed model has excellent performance in many evaluation scales, especially accuracy and stability. Compared with conventional methods, the model of this study has reduced the error rate by 15%, confirming its practical value in distance education quality assessment. This exploration has opened a new path for distance education quality evaluation and looks forward to broad application potential.