To address the issues of unbalanced load and system nonlinearity in quadrotor unmanned aerial vehicles（UAV）attitude control, a LSTM-MPC strategy is proposed by combining the advantages of long short-term memory（LSTM）neural network and model predictive control（MPC）．LSTM neural network is used to predict attitude changes, enhancing the system' s ability to anticipate errors．MPC is employed as feedforward control to dynamically optimize control inputs．The combination significantly improves system control accuracy．MATLAB simulation experiment on quadrotor UAV attitude control with unbalanced load shows that：compared to MPC strategy, the LSTM-MPC strategy reduces the root mean square error of tracking expected values for roll angle, pitch angle, and yaw angle by 13. 33%, 12. 31%, and 11. 11% respectively; compared to fuzzy PID strategy, it reduces by 14. 05%, 25. 33%, and 23. 81% respectively．Flight test is conducted using a branded F450 quadrotor UAV platform carrying a 0. 6 kg load to test unbalanced load attitude control．The test result shows that the average errors between the actual output and expected values of the quadrotor UAV's roll, pitch, and yaw angles using the LSTM-MPC strategy are 3. 91%, 5. 31%, and 1. 10%, respectively, indicating that the LSTM-MPC strategy can effectively improve the flight stability of quadrotor UAV attitude control with unbalanced load．