As audiovisual translation (AVT) becomes more complex and diverse, the need for advanced machine learning techniques has been increasing sharply, driving the widespread adoption of neural machine translation (NMT) technology in the field. This study contributes to the literature by evaluating the performance of NMT technology in AVT teaching. Based on readability theory, the authors constructed an evaluation framework with 12 indicators, built comparable corpora consisting of human and post-edited subtitle translations of corporate videos, and used them to examine the performance of four online NMT systems (Google Translate, Baidu Translate, Bing Translator, and Youdao Translate) in AVT teaching. The statistical analyses and case studies show that Google Translate outperforms the other three platforms in all the readability tests, and it can enhance the readability of post-edited subtitles at five levels (word, syntax, textbase, situation model, genre and rhetorical). The performance of the other three platforms varies across different tests. Concrete examples are provided to substantiate the statistical analyses. The study adds value to existing research both by examining the application and performance of NMT in AVT teaching and by suggesting potential directions for the refinement of NMT systems.