<p>Smart education has emerged as a critical global trend in educational transformation, with the primary goal of fostering learners’ deep learning. Smart learning environments (SLEs), as the main platforms for implementing smart education, offer advanced technological affordances. However, the mechanisms through which they actually facilitate deep learning remain underexplored, and their potential has not been fully translated into learning outcomes. To address this research gap, this study drew on Triadic Reciprocal Determinism and a systematic review of the literature to identify eight influencing factors through expert consultation and to develop a hypothetical model of college students’ deep learning in SLEs. Based on data from 605 valid questionnaires, structural equation modeling was employed to test the proposed model. The results revealed that self-regulation and teacher factors significantly promoted deep learning strategies, whereas self-efficacy and teacher factors were significant predictors of deep learning motivation. Student-student interaction positively influenced learning engagement, while course factors unexpectedly exerted a negative effect. Moreover, deep learning motivation, engagement, and strategies all had significant positive impacts on learning outcomes. This study revealed the multidimensional factors influencing deep learning in SLEs and clarified their underlying mechanisms. From a practical perspective, the findings highlight the importance of fostering students’ self-directed learning and intrinsic motivation, enhancing teachers’ professional competence, and optimizing interaction and course resources in order to strengthen the role of SLEs in supporting deep learning.</p>