【摘要】 大数据在教育领域的应用日益广泛，目前学生行为识别分析模型仍存在数据处理效果和模型泛化能力不足的问题。鉴于此，本文提出基于改进布谷鸟搜索算法和模糊C均值聚类的高校学生行为识别分析模型。实验结果表明，本文提出的模型F₁分数平均值达到0.982 6，几何平均数的平均值为0.902 3，优于其他对比模型。模型的曲线下面积值最高达到了0.952。在聚类效果方面，模型的聚类误差平方和最低146，轮廓系数分布集中在0.8～0.9之间，且散点分布最为集中。在实际应用中，模型可以分析出学生的社交关系，从而展现了良好的实用性。本文为高校教育管理与学生发展评估提供了更精准的数据支持和决策依据，推动了大数据技术在教育领域的深度应用。更多还原

【Abstract】 The application of big data in the field of education is becoming more and more extensive. At present,there are still some problems in the data processing effect and model generalization ability of student behavior recognition and analysis model. In view of this,this paper proposes an analysis model of college students’ behavior recognition based on improved cuckoo search algorithm and fuzzy C-means clustering. The experimental results show that the average F₁ fraction of the proposed model is 0.982 6,and the average geometric mean is 0.902 3,which is superior to other models. Secondly,the AUC value of the model reaches up to 0.952. In terms of clustering effect,the sum of squares of clustering error of the model is the lowest 146,and the distribution of contour coefficients is concentrated between 0.8～0.9,and the scatter distribution is the most concentrated. In addition,in the analysis of students’ social relations with different feature dimensions,the F₁ value,recall rate and accuracy rate all exceed 0.90,showing good generalization ability and practicability. The research provides more accurate data support and decision-making basis for higher education management and student development assessment,and promotes the in-depth application of big data technology in the field of education.更多还原