Trajectory prediction is part of air combat technology，and the predictors can select a more predictable maneuvering considerion of trajectory prediction results. A convolution neural network predicting method is proposed to obtain the position of unmanned combat aerial vehicle in a future time quickly and accurately. An improved model for limiting the angular velocity is presented since the original dynamic model can not correctly simulate the somersault maneuvering with roll angle deviation. The improved model is used for flight simulation under different conditions，and a large number of trajectory samples are obtained. The convolution neural networks with different layer number and convolution kernel number is trained and tested，and the network with the smallest prediction error is selected. Operational speed and error of the proposed method are compared with those of long short term memory neural network，recurrent neural network and fully connected neural network. The results show that the average prediction error of the proposed method is about 4.2 m on x axis，8.0 m on y axis and 19.5 m on z axis after 0.25 s without increasing operational time，and the errors are all smaller than those of the other methods.