The conventional analysis method can provide a general analysis of sports training index, but its ability is relatively low when analyzing niche data. To solve this problem, this paper proposes data mining technology. First, the indicator parameter classification is determined, then the data mining technology is imported, the sports training analysis mechanism is established through this technology, and the construction of the index analysis model is completed. The model is used to analyze the process of niche data mining, and effective data of training indicators are obtained. Deep learning is a method of machine learning based on the representation of data. Through the coverage test, accuracy test, and immunity test, the variable parameters of the comprehensive analysis capability are determined. Further calculation of this parameter shows that the comprehensive ability of the data mining application analysis method is improved by 37.14% compared with the conventional method, which is suitable for the analysis of niche sports training indicators of different data types.

常规的分析方法可以提供运动训练指标的一般分析，但是在分析利基数据时其能力相对较低。为了解决这个问题，本文提出了数据挖掘技术。首先确定指标参数分类，然后引入数据挖掘技术，通过该技术建立运动训练分析机制，完成指标分析模型的构建。该模型用于分析小生境数据挖掘的过程，并获得有效的训练指标数据。深度学习是一种基于数据表示的机器学习方法。通过覆盖率测试，准确性测试和抗扰度测试，确定了综合分析能力的可变参数。