Abstract
To enhance the execution of growth strategies for SMEs under data sparsity and domain shift, this study combines domain adaptation with few-shot learning to identify growth bottlenecks and generate actionable strategies through model optimization. Practical case studies validate the model's applicability and adaptability across domains. By integrating feature alignment and reweighting mechanisms, the strategy significantly improves performance in long-tail categories and cross-domain transferability.
Keywords
SMEsGrowth StrategiesDomain AdaptationFew-shot LearningStrategy Optimization
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Cite This Article
Liu, W. (2025). Few-shot and domain adaptation modeling for evaluating growth strategies in long-tail small and medium-sized enterprises. Journal of Industrial Engineering and Applied Science, 3(6), 30-35. https://doi.org/10.70393/6a69656173.333434
Acknowledgments
The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.
Funding
Not applicable.
Institutional Review Board Statement
Not applicable.
Data Availability Statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Informed Consent Statement
Not applicable.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Author Contributions
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References
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Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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