The most significant material in teaching quality assessment is the teacher’s teaching evaluation; nevertheless, as education informatization accelerates, the current teaching management challenge is how to use network technology to evaluate instructors’ teaching quality in conventional teaching. The question of how to evaluate the network teaching environment is a crucial issue in the development of network teaching. The key to educational evaluation lies in the quality of teaching. Therefore, the effective processing and analysis of the huge original data collected in the teaching process of colleges and universities can provide decision support for the evaluation of teaching quality and the formulation of relevant improvement measures. Given the variety and large quantity of original teaching data, this paper proposes a deep learning-based teaching quality evaluation model that organically integrates various original data by constructing deep neural networks and can achieve more accurate teaching quality evaluation, which has practical value. Education evaluation is a complex system engineering, which needs to consume a lot of manpower, material, and financial resources. Through the use of the system to obtain a large number of statistical data, it will provide a basis for in-depth analysis and decision-making.