This study aims to enhance the accuracy and practicality of thrust estimation models for aero engines. The research first constructs a multi-task LSTM-Attention model that integrates Long Short-Term Memory (LSTM) and attention mechanisms for time series forecasting. Additionally, to address the issue of thrust estimation under different flight conditions, this paper employs Fine-tuning and an improved Domain-Adversarial Neural Network (DANN) transfer learning method to strengthen the model"s adaptability to multiple operational conditions. The results demonstrate that LSTM combined with the attention mechanism can effectively model long time series data, rectifying LSTM"s insufficiency in global modeling capabilities, while also overcoming the limitation of the attention mechanism in capturing relative position information. The multi-task learning strategy can significantly improve the model"s prediction accuracy at the abrupt changes in the throttle levers, further enhancing the model"s accuracy. The study of thrust prediction under different conditions based on transfer learning methods indicates that Fine-tuning should be selected when there is limited target domain data, while the modified DANN method will yield a model with higher accuracy when there is sufficient target domain data. This research provides a more accurate solution to the problem of thrust estimation for aero engines and has significant reference value for future research and practical applications.