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Analysis of factors affecting high-quality employment of college students based on deep learning and fuzzy algorithm

Jiayi Shi, Nan Wang

Discover Internet of Things 2026 Vol. 6(1), pp. 245
https://doi.org/10.1007/s43926-026-00502-8

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چکیده

Traditional statistical models face the challenges of insufficient modeling capabilities and difficulty in interpreting complex, nonlinear, and high-dimensional data. Therefore, this paper combines fuzzy logic and deep learning methods to construct a framework model for identifying key factors for high-quality employment for college students. The model in this paper uses multi-channel encoding technology. We use the attention mechanism to fuse the features obtained from multi-channel encoding and input the self-organizing fuzzy inference structure for regression and classification analysis. The system can transform numerical prediction results into a semantic rule-driven decision-making process, which can significantly improve the model’s modeling capabilities and interpretability for uncertainty. Our experiments were conducted on 5,623 graduates. Experimental results on the data show that our model achieved an average mean square error of 0.831, a coefficient of determination of 0.892, and a classification accuracy of 0.865, which are significantly higher than traditional statistical and machine learning models. The corresponding experiments demonstrate the key role of cross-modal fusion and fuzzy reasoning modules in improving the results. Feature importance analysis shows that factors such as internship duration, GPA, and research participation have a key impact on high-quality employment, which is consistent with expert consensus.

موضوع‌ها و کلیدواژه‌ها

دانشجویانروان‌شناسی سازمانیروانشناسی شغلیInterpretabilityattention mechanismcross-modal feature fusionemployment factorsfuzzy logichigh-quality employmentuncertaintyuniversity graduates

ارجاع

Shi, J., & Wang, N. (2026). Analysis of factors affecting high-quality employment of college students based on deep learning and fuzzy algorithm. Discover Internet of Things, 6(1), 245. https://doi.org/10.1007/s43926-026-00502-8

(Shi & Wang, 2026)

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قوانین پرسش و گفتگو
  1. با احترام و بدون توهین، تحقیر، برچسب‌زنی یا حمله‌ی شخصی با دیگران گفتگو کنید.
  2. از نوشتن هرگونه اطلاعات هویتی یا قابل‌شناسایی مراجعان، بیماران یا اشخاص ثالث، از جمله نام، شماره‌ی تماس، تصویر، نشانی، محل کار یا جزئیات منحصربه‌فرد پرونده خودداری کنید.
  3. اگر درباره‌ی یک تجربه‌ی بالینی سؤال می‌کنید، اطلاعات را تا حد ممکن بی‌نام و کلی بیان کنید تا امکان شناسایی فرد وجود نداشته باشد.
  4. این بخش برای یادگیری و گفت‌وگوی علمی است و جایگزین ارزیابی، تشخیص، مشاوره یا روان‌درمانی شخصی نیست.
  5. در شرایط بحران یا خطر آسیب به خود یا دیگران، به این بخش اتکا نکنید و برای دریافت کمک فوری با اورژانس اجتماعی ۱۲۳ یا صدای مشاور بهزیستی ۱۴۸۰ تماس بگیرید.
  6. انتشار تبلیغات، معرفی خدمات شخصی، شماره‌ی تلفن، شناسه‌ی شبکه‌های اجتماعی، پیوند تبلیغاتی یا درخواست جذب مراجع در این بخش مجاز نیست.
  7. در بیان ادعاهای علمی، به‌ویژه درباره‌ی اثربخشی درمان‌ها، تشخیص یا یافته‌های پژوهشی، تا حد امکان نام منبع معتبر، مقاله یا راهنمای علمی را ذکر کنید.
  8. از ارائه‌ی تشخیص قطعی، تجویز درمان یا دارو و توصیه‌های بالینی شخصی به سایر کاربران خودداری کنید.
  9. پاسخ‌هایی که کارشناسان فکر بنیان می‌دهند با برچسب «پاسخ فکر بنیان» یا «کارشناس فکر بنیان» نمایش داده می‌شوند و سایر نوشته‌ها بیانگر نظر کاربران هستند.
  10. گزارش نوشته‌ی نامناسب: اگر نوشته‌ای حاوی نقض محرمانگی، محتوای آسیب‌زا، توهین، تبلیغ یا تخلف از این قوانین است، گزینه‌ی «گزارش» را انتخاب کنید تا تیم سایت بررسی کند.

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