Identifying network traffics at their early stages accurately is very important for network management and security. Recent years, more and more studies have devoted to find effective machine learning models to identify traffics with few packets at the early stage. In this paper, we try to build an effective early stage traffic identification model by applying flexible neural trees (FNT). Three network traffic data sets including two open data sets are used for the study. We first extract both packet-level features and statistical features from the first six continuous packets and six noncontinuous packets of each flow. Packet sizes are applied as packet-level features. And for statistical features, average, standard deviation, maximum and minimum are selected. Eight classical classifiers are employed as the comparing methods in the identification experiments. Accuracy, true positive rate (TPR) and false positive rate (FPR) are applied to evaluate the performances of the compared methods. FNT outperforms the other methods for most cases in the identification experiments, and it behaves very well for both TPR and FPR. Furthermore, it can show the selected features in the optimal tree result. Experiment result shows that FNT is effective for early stage traffic identification.