Translation technology education represents a critical yet underdeveloped aspect in the training of Bachelor of Translation and Interpreting (BTI) students in higher education institutions. This paper delves into the implications of the emergent liberal arts paradigms on BTI education. It addresses the challenges inherent in existing BTI translation technology courses by proposing a novel instructional system rooted in machine learning. Specifically, the system utilizes a deep learning Sequence-to-Sequence (Seq2Seq) model to facilitate the transformation of source language text sequences into target language equivalents. Furthermore, the system integrates collaborative filtering to tailor content recommendations, thereby achieving functionalities such as real-time translation, personalized content suggestion, and adaptive learning. Subsequent to the system’s validation, the translation technology curriculum was restructured, and its efficacy was evaluated through a controlled pedagogical trial spanning one academic year. Performance metrics from the system’s deployment indicate robust outcomes in both development and test environments, with Bilingual Evaluation Understudy (BLEU) scores of 26.79, 26.34, 24.15, and 24.52, respectively—each surpassing competing models by a minimum margin of 2.5 points. The pedagogical trial further evidenced substantial academic gains; the average score of students in the experimental cohort rose from 12.79 to 17.44, marking an increase of 4.65 points. These enhancements substantiate the efficacy of both the bespoke BTI instructional translation system and the redesigned course framework. This investigation furnishes valuable insights and viable strategies for the amalgamation of information technology with BTI education, as well as for the innovation of translation technology curricula. It contributes to the enhancement of the BTI curriculum architecture within universities and promotes the evolution of translation technology pedagogy.