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With the widespread use of digital technologies such as big data, cloud computing and artificial intelligence in higher education, how to establish a scientific and systematic evaluation system to turn the traditional classroom with the one-way transmission of knowledge into an interactive space for exchanging ideas and inspiring wisdom has become an essential task for human resource management in universities, and a key to improving teaching quality. However, due to the debate between scientism and humanism in teaching evaluation, studies related to teaching performance have been isolated from human resource management, resulting in the lack of a systematic vision and framework for such studies. Relevant studies are still limited to the evaluation contents of different evaluation subjects. Evaluations also tend to focus only on the teaching process, ignoring the objectives of talent training, making it difficult for evaluations to play a goal-oriented role and hindering the further development of relevant studies. Therefore, this paper draws on human resource management methodologies and analyzes knowledge teaching evaluation system characteristics in colleges and universities in a big data context to construct a “multiple evaluations, trinity and four-step closed-loop” big data-based knowledge teaching evaluation system. “Trinity” represents evaluation from three performance dimensions: teaching effect, teaching behavior and teaching ability. “Multiple evaluations” represents the design of teaching performance indicators based on teaching data, breaking the barriers between different evaluation subjects. “Four-step closed-loop” draws on performance management theory to standardize the teaching performance management process from four aspects: planning, implementation, evaluation, and feedback. This evaluation system provides a systematic methodology for unifying the theory and practice of innovative knowledge teaching evaluation system in universities in a big data context.