In this study, a novel method for real-time trajectory prediction of aircraft with non-cooperative targets is proposed. Leveraging an Encoder-Decoder LSTM (ED-LSTM) model, we achieve accurate multi-step trajectory predictions while ensuring computational efficiency. Our approach strikes a balance between prediction accuracy and real-time responsiveness, addressing a challenging aspect of trajectory prediction. The effectiveness of our model is highlighted through evaluations using RMSE and \(R^{2}\) metrics. Specifically, in a rapidly changing scenario with an average motion speed of 200 m per second, the calculated predicted position errors averaged 5.7 m at 1 s, 32.1 m at 3 s, and 82.1 m at 5 s. Notably, our model demonstrates effectiveness in predicting changes in the motion trend of non-cooperative targets. Furthermore, real-time performance analysis reveals that over 99% of predictions meet the 100 ms real-time requirement. While our approach holds promise, further validation on real-world datasets is warranted to assess its generalizability. Ultimately, this study contributes to the advancement of trajectory prediction methodologies, offering practical implications for aviation and related disciplines.