Abstract: Deep learning is a general trend of international education reform and a core requirement for the development of core competencies. How to evaluate deep learning is a challenging task related to the development of deep learning. Bloom’s taxonomy of educational goals, Weber’s depth of knowledge model, and the multi-dimensional theoretical system of deep learning evaluation provide a solid theoretical basis for deep learning evaluation. Intelligent technologies such as facial expression recognition, speech recognition, natural language processing, and block chain have great application prospects in the collection, processing and storage of evaluation information. The results of the case studies demonstrate that technology-enabled deep learning assessments are playing an increasingly important role in the context of online learning. Looking forward to the future of intelligent deep learning evaluation, we conclude that quantitative ethnographic methods provide methodological guidance for deep learning evaluation.

Key words: deep learning, depth of knowledge, performance evaluation, intelligent technology, quantitative ethnography