Affected by a large number of radio frequency interference signals, it has become an important task for astronomical data processing to quickly and accurately identify single pulse signals from massive observation data. Designing and extracting effective data features is the key issue for efficient identification of single pulse signals using machine learning. This paper proposes an ensemble feature selection method for single pulse signal classification. The method first mixed three types of features, including the parametric features, statistical features and abstract features of single pulse signals, and then used five independent feature selection methods to select the corresponding optimal feature set, respectively. At last, the features selected by the five independent methods are mixed and the greedy strategy was used to select the optimal ensemble feature set. The experimental results show that the ensemble feature set can improve F1-score by value of 1.8% at most and can obtain higher accuracy than the features selected by independent methods. Under the background of high-speed and large-scale sky survey, the ensemble feature selection method plays an important role in reducing the number of features, improving classification performance and speeding up data processing.