In recent years, deep learning has achieved breakthrough progress in radar target recognition. However, research on deep learning target recognition algorithms based on radar cross-section (RCS) data is relatively scarce. Additionally, space target radar signals are easily affected by noise, resulting in low target recognition accuracy. This paper proposes an end-to-end Time-Frequency Feature Fusion Neural Network (TFF-Net) for space target recognition based on RCS sequence data. First, time-frequency analysis methods are used to convert the RCS sequence data into two-dimensional time-frequency data to reduce noise interference. Then, TFF-Net is used to extract deep features from the time-frequency data. TFF-Net first uses a convolutional neural network to capture spatial features of the targets, then employs a bidirectional long short-term memory network to model temporal information, and finally applies a temporal attention network to adaptively focus on important sequences in the time-frequency data. Comparative experiments were conducted on a space target dataset. The results show that the proposed algorithm achieves a space target recognition accuracy of 95.8%, significantly higher than several current mainstream radar target recognition algorithms. Furthermore, the classification accuracy under low signal-to-noise ratio conditions is also superior to other algorithms, demonstrating better noise robustness.