Against the backdrop of digital transformation in education, the integration of Mixed Reality (MR) and Artificial Intelligence (AI) has fostered a new “virtual-physical symbiotic” learning environment. However, current educational tools often fail to capture learners’ dynamic needs and the characteristics of 3D immersive scenarios, resulting in a disconnect between virtual character interactions and the teaching process. Existing approaches face various limitations: rule-based models struggle with the dynamics of three-dimensional spaces; collaborative filtering algorithms overlook spatial contextual features; and deep learning models lack joint modeling of emotion and context. These shortcomings highlight insufficient multi-source data integration and weak situational awareness. To address these challenges, this study proposes a virtual character recommendation method tailored to immersive learning environments. The model is composed of a transformation layer, a self-adversarial data generation layer, an embedding representation layer, and a virtual character prediction layer. It achieves dynamic matching between virtual characters and 3D interactive scenarios by incorporating multi-source data standardization, intelligent agent-based game simulation for scenario data generation, semantic vectorization of character features, and Long Short-Term Memory (LSTM)-attention mechanism fusion. This research marks the first application of MR spatial computation and self-adversarial learning in educational role recommendation, offering a technical framework to tackle adaptation challenges in immersive scene recommendations. The proposed approach contributes both theoretical innovations and practical guidance for the advancement of intelligent education.