Due to the large amount and variety of LTE network data, the manual drive test analysis has been unable to meet the current requirements for poor quality cell detection based on drive test data. In order to improve the efficiency and accuracy of the poor quality cell detection, machine learning is gradually applied in the detection of poor quality cell. In this study, a poor quality cell detection method based on four-dimensional feature of distance is proposed for the small number of road survey data. This method uses clustering algorithm and artificial judgment to calibrate road test data. And it compares the extraction effect of the distance based four-dimensional features and the traditional two-dimensional features. The featuresare classified by logistic regression classifier, decision tree classifier, support vector machine classifier and k-nearest neighbor classifier. The experimental results show that the distance-based four-dimensional features are more beneficial to the detection of quality difference cells than the traditional two-dimensional features. Support vector machine classifier works best when four-dimensional features are used for classification.