In recent years, the awareness of sports departments at all levels of society to promote sports through science and has been increasing, and the scientific decision-making and management of sports have been improved to a great extent. With the application of scientific decision-making combined with a real-time sports data monitoring network, the opponent’s advance information can be effectively observed during the game and reasonable decisions can be made to deal with the opponent’s offense. Therefore, high-level athletes appear to be more relaxed and calm in the game. It first requires the application of advanced information collection methods to obtain sports data quickly, in real time and at low cost, and extract information about athletes’ scientific management decision-making from massive data and then make scientific management decisions for sports training. The modern sports method is highly open, and big data mining also profoundly affects the relevant decision-making of sports training. How to design appropriate decision support tools to grasp the key points of the problem in sports information data and make reasonable and correct decisions is a problem that is closely watched by macro decision-makers and coaches at all levels. This article mainly introduces the training decision support method derived from data mining and intends to provide some technical directions for making scientific decisions in sports training. This paper proposes related algorithms of a training decision support method derived from data mining, including training effectiveness prediction model and decision tree algorithm, for the design of the training decision support method derived from data mining. Experimental data shows that the average error between the prediction of the effectiveness of the training method and the actual situation of the training decision support method in this paper is 0.913%, which is helpful for the management or coach to make decisions.

The evaluation indicator of teachers’ scientific research is a very important criterion in the evaluation of teachers’ scientific research in colleges and universities. Generally, indicators are divided into three levels, and each indicator setting must be very precise. Many indicator systems for college teachers have been created at present. However, there are some problems in the identification of indicators in the evaluation process. There will be cross-repetition between indicators, and the quantification of indicators is not detailed enough, resulting in inaccurate results. To solve these problems and make the evaluation results more fair and accurate, this paper took a university as a case, a deeply discussed method based on wireless communication network, and conducts an experimental analysis on the identification of university teachers’ scientific research evaluation indicators. Using the method of wireless communication network, the problem of index identification was analyzed, and the experimental research of index identification was carried out by using wireless communication network. The results showed that the index of engineering teachers’ practical ability was relatively high, and the weight value was 0.38. The liberal arts was relatively low, and the weight value was only 0.26. However, on the cognitive index, the weight value of liberal arts was the highest, with the weight value of 0.33 while that of engineering was only 0.28. It can be seen that the method applied in this paper greatly improved the efficiency of the whole evaluation, and it made the index recognition more rapid and accurate. Therefore, further research on wireless communication networks and index identification can be considered.