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A temperature noise correction method for CMOS space cameras based on attention mechanisms and LSTM is presented. This method relates to the field of optical remote sensing technology, specifically to the field of temperature noise correction for CMOS space cameras. The core of the method is the use of a multi-level Long Short-Term Memory (LSTM) network with an attention mechanism to explore how temperature changes affect the noise performance of CMOS space cameras under two different operating conditions: dark and bright fields. This deep learning model possesses powerful autonomous learning capabilities, capable of mining and understanding complex noise patterns hidden within massive amounts of data, thereby improving the accuracy of noise identification and calibration. Its key advantage lies in its end-to-end learning approach, automatically learning and establishing a deep, nonlinear relationship between temperature and noise directly from the raw data, without requiring manual intervention for feature extraction or designing complex correction algorithms. This significantly enhances the model's generalization ability and adaptability to unseen data scenarios.