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<p>Innovation and entrepreneurship (I&amp;E) are crucial to promoting economic development and sustainability, particularly in institutions of higher learning. Though much has been invested in I&amp;E initiatives, the success rates of projects proposed by students are low because I&amp;E is a complex and uncertain venture. The objective of this research is to enhance the assessment process of college students’ I&amp;E projects using a fuzzy algorithm-based framework that combines both subjective expert opinion and objective data analysis. The developed framework includes internal and external determinants of project success, including curriculum quality, mentorship, policy support, and industry collaboration. Fuzzy entropy is utilized to dynamically set weights for these factors based on their relative importance in each project situation. In addition, fuzzy membership functions are used to convert qualitative assessments into quantitative values, and a fuzzy evaluation matrix is constructed to aggregate weighted factors for final decision-making. The fuzzy integrated assessment approach is employed to evaluate the I&amp;E project’s success rate, converting qualitative judgments into quantitative measures of success. Findings indicate that the framework can improve the accuracy of evaluation by a significant margin and that the majority of projects were rated high. Out of the 150 projects rated, 71 (47.3%) were excellent, 61 (40.7%) were good, 7 (4.7%) were average, and 11 (7.3%) were poor, with an average rating of 72.7. Here, ‘success’ refers to the model-based evaluation outcome derived from the fuzzy scoring mechanism (score ≥ 70), rather than external real-world outcomes such as winning a competition, receiving funding, or remaining operational after two years. The probability of success for projects with a rating over 70 was 98.7%, which has a strong correlation between higher-rated scores and project success. These observations indicate that the fuzzy algorithm-based evaluation model can efficiently determine improvement areas and contribute to ongoing improvements in I&amp;E project success rates.</p>

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Exploration on Improving the Success Rate of College Students’ Innovation and Entrepreneurship Projects by Using Fuzzy Algorithm

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Wenzhao Zhang

摘要

Innovation and entrepreneurship (I&E) are crucial to promoting economic development and sustainability, particularly in institutions of higher learning. Though much has been invested in I&E initiatives, the success rates of projects proposed by students are low because I&E is a complex and uncertain venture. The objective of this research is to enhance the assessment process of college students’ I&E projects using a fuzzy algorithm-based framework that combines both subjective expert opinion and objective data analysis. The developed framework includes internal and external determinants of project success, including curriculum quality, mentorship, policy support, and industry collaboration. Fuzzy entropy is utilized to dynamically set weights for these factors based on their relative importance in each project situation. In addition, fuzzy membership functions are used to convert qualitative assessments into quantitative values, and a fuzzy evaluation matrix is constructed to aggregate weighted factors for final decision-making. The fuzzy integrated assessment approach is employed to evaluate the I&E project’s success rate, converting qualitative judgments into quantitative measures of success. Findings indicate that the framework can improve the accuracy of evaluation by a significant margin and that the majority of projects were rated high. Out of the 150 projects rated, 71 (47.3%) were excellent, 61 (40.7%) were good, 7 (4.7%) were average, and 11 (7.3%) were poor, with an average rating of 72.7. Here, ‘success’ refers to the model-based evaluation outcome derived from the fuzzy scoring mechanism (score ≥ 70), rather than external real-world outcomes such as winning a competition, receiving funding, or remaining operational after two years. The probability of success for projects with a rating over 70 was 98.7%, which has a strong correlation between higher-rated scores and project success. These observations indicate that the fuzzy algorithm-based evaluation model can efficiently determine improvement areas and contribute to ongoing improvements in I&E project success rates.