The rapid development of large language model technology has evolved machine translation from a low-level tool into a cultural transmission vehicle with semantic understanding capabilities, shifting the relationship between artificial intelligence and human translators from one of substitution to one of collaboration.Employing Translator Behavior Criticism theory and comparative analysis, this study systematically analyzes the behavioral characteristics of student translators and multi-model machine translators across the two dimensions of "truth-seeking" and "utility-attaining," revealing the differential patterns between human and machine translators in three aspects: semantic fidelity, cultural adaptability, and audience orientation.The findings indicate: 1) Student translators demonstrate stronger subjectivity in terms of cultural awareness and ideological expression, enabling a deeper grasp of the philosophical connotations and value orientation of terminology; 2) Machine translators hold significant advantages in lexical innovation and adaptation to linguistic norms, yet exhibit notable limitations in understanding complex rhetorical structures and cultural metaphors; 3) Humanmachine collaborative pathways can achieve a more optimal balance of tension between preserving Chinese characteristics and achieving international accessibility, forming a bidirectional enhancement effect characterized by "complementarity between truth-seeking and innovation, and integration of utility-attaining and flexibility"; 4) A collaborative translation system requires the construction of a three-tier progressive mechanism of "multi-model inspiration-in-depth student revision-expert feedback optimization" to realize the organic unity of cultural confidence and international communication.