Abstract: The outbreak of novel coronavirus (2019-nCoV) pneumonia occurred at the end of 2019. The epidemic situation makes us more aware of the importance of emergency rescue team construction and related personnel training. On the basis of participants in national disaster life support (NDLS) training data from 2018 to 2019, this paper is to study influence factors of NDLS training effects with machine learning method with a view to assisting organizations in evaluating the training effects， using Apriori and decision tree algorithm to build the model. Through data preprocessing, 14 fields are selected as model input fields, and test scores are selected as model output field. Firstly, Apriori algorithm is used to find out several factors that have a great influence on the training effect. Then the decision tree model is used to predict the training effects, and the results of decision tree are used to verify the conclusion of Apriori algorithm. The parameters of confidence, support and promotion are used as the evaluation indexes of Apriori algorithm. Ten fold cross validation is used as the evaluation method of decision tree model. The results show that the model is effective. Conclusion of this study can help the training organizations to effectively predict the learning effects of the trainees; in addition, it can monitor and ensure the quality of training.

Key words: machine learning, national disaster life support (NDLS), influence factors, Apriori algorithm, decision tree