目的 分析军校大学生5000 m跑成绩与相关运动生理指标的关系,为进一步科学安排训练、提高军人耐力素质提供理论依据。方法 整群抽取58名某军校大学生,测试5000 m跑成绩,以及身体形态、心肺功能、速度素质和力量素质等四类共12项运动生理指标。建立灰色关联分析模型对采集的数据进行分析,运用BP神经网络模型验证评价效果。结果 影响5000 m跑成绩的前5项主要因素依次为最大摄氧量(VO_2max)、5000 m定量负荷心率、2000 m跑成绩、体脂百分比和肺活量指数。VO_2max和5000 m定量负荷心率能通过BP神经网络模型准确评价5000 m耐力素质。结论 灰色关联分析模型筛选出的影响因素合理可信。应着重考虑安排有助于增强心肺功能和降低体脂百分比的训练项目,以减少训练的盲目性,提高5000 m耐力素质训练质量。

Objective To analyze the correlation between the performance of military academy students’ 5000-meter running and their exercise physiology indicators associated with 5000-meter running so as to make further efforts to arrange training projects scientifically and provide a theoretical basis for military academy to improve the training quality of 5000-meter running. Methods 5000-meter running performance and 12 exercise physiological indexes grouped into four categories (including anthropometry, cardiorespiratory function, velocity fitness and strength fitness) of 58 students from a military academy was measured by cluster sampling method. Gray correlation model was established to analyze the collected data. Then BP neural network model was used to verify the evaluation results. Results According to the order, the five main factors influencing the performance of 5000-meter running were VO_2max, heart rate of 5000-meter running at fixed workload, performance of 2000-meter running, fat rate and FVC index.BP neural network model was able to accurately evaluate the endurance performance of 5000-meter running with VO_2max and heart rate of 5000-meter running at fixed workload. Conclusion The factors screened by gray correlation model are reasonable and credible. Suggest military academy should consider arranging some training projects which contribute to improvement of cardiopulmonary function and drop of fat rate. By this way, the blindness of training is reduced so as to improve the training quality of 5000-meter running.