In order to better mine the effective information contained in the UAV flight path data and evaluate the UAV flight quality accurately and objectively based on the trajectory data, a CNN-BiLSTM network model which integrates convolutional neural network (CNN) and bi-directional long short-term memory (LSTM) neural network is proposed. Firstly, CNN and BiLSTM are applied to obtain the local convolution feature and time feature of flight path data respectively, and then the two features are sent to the feature fusion layer, and the fused features are used to classify and get the rating label. The numerical experiments on six datasets show that the model not only achieves good classification results, but also has good generalization ability.