When the global navigation satellite system （GNSS） signal is unavailable， the performance of GNSS/inertial navigation system （INS） integrated navigation system significantly degrades， which leads to the rapid divergence of INS errors of UAV swarms. At present， the neural network model is used to predict the position and speed instead of GNSS navigation information to correct the positioning error of the INS. However， this method suffers from high positioning errors and a sharp decline in prediction accuracy when the trajectory changes suddenly. Therefore， a position and velocity prediction method based on convolution neural networks （CNN） -bidirectional long short-term memory network （BiLSTM） joint residual compensation model is proposed to compensate for inertial navigation errors and improve position and velocity positioning accuracy. Firstly， aiming at the problem of high positioning error of GNSS/INS integrated navigation system after GNSS denial， a time series prediction network is formed by fusing CNN and BiLSTM to train and establish the relationship between inertial measurement unit （IMU） dynamics measurement and GNSS information， so as to realize position and speed prediction. Secondly， aiming at the problem that the prediction effect drops sharply when the trajectory changes abruptly，CNN-BiLSTM is used again to mine the relationship between the IMU dynamics measurement， prediction value and prediction residual， and to predict and compensate the prediction residual. Simulation results show that the proposed model outperforms traditional CNN-LSTM and LSTM network models in terms of prediction accuracy， effectiveness， and stability.