This study integrates BERT with multi-source data fusion technology to construct a chain-mediated model linking practical education base construction, teacher guidance effectiveness, collaborative education, and employability improvement. It uses a domain-adapted fine-tuned BERT for deep semantic encoding of unstructured internship texts and designs a "feature-layer attention + decision-layer GBDT"framework to integrate diverse data, building a high-precision employability prediction model. Empirical findings show: (1) Practical education base construction directly enhances employability, with its interaction with teacher guidance effectiveness indirectly influencing employability through the chain-mediation path of collaborative education (accounting for 59.15% of total mediation effect); (2) BERT-parsed textual semantic features contribute most to employability prediction (38.7%), verifying the technology's key role in evaluating implicit abilities. The study provides quantitative evidence for collaborative education mechanisms, offers scalable technical solutions for multi-source data fusion and intelligent analysis, and proposes technology-empowered optimization paths covering institutional guarantees, teacher evaluation reform, enterprise platform development, and students' reflective practice. © 2025 Copyright held by the owner/author(s).
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