Abstract: Objectives: The difference between Universal Time and Coordinated Universal Time (UT1-UTC) is a critical component of the Earth Orientation Parameters (EOP). Accurate and rapid prediction of UT1-UTC is essential for real-time applications such as GNSS meteorology and precise orbit determination of artificial satellites. Traditional methods for predicting UT1-UTC often experience significant accuracy degradation in medium- and long-term forecasts, making them inadequate for high-precision requirements in applications like the BeiDou Navigation Satellite System and precision guidance in military operations. Methods: We propose a novel method for predicting UT1-UTC by integrating the axial component data χ₃ of Effective Angular Momentum (EAM) with the EOP14 C04 series using a Convolutional Long Short-Term Memory (ConvLSTM) model. Initially, leap seconds and solid Earth tide terms are removed from the original UT1-UTC series to obtain UT1R-TAI data. Spectral analysis is then performed on the axial component data χ₃ of EAM and UT1R-TAI data using the fast Fourier transform (FFT) to investigate whether the axial component data χ₃ of EAM can comprehensively describe the excitation of UT1-UTC. Subsequently, a ConvLSTM model incorporating the axial component data χ₃ of EAM is constructed to predict the UT1-UTC time series. After prediction, the leap seconds and solid Earth tide terms are reintroduced, while the accuracy of predictions is evaluated. Results: Analysis of observations reveals a strong correlation between the axial component χ₃ of EAM and UT1R-TAI data, with consistent amplitude and phase characteristics in their frequency spectra. This indicates that the axial component χ₃ of EAM serves as a primary excitation source for UT1-UTC. Compared to the prediction accuracy of participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), the ConvLSTM model demonstrates superior performance in medium- to long-term predictions spanning 90 to 360 days, with accuracy improvements ranging from 30.27% to 92.44%. Additionally, compared to Bulletin A, the ConvLSTM model achieves accuracy enhancements of 41.46%, 70.07%, and 59.43% for prediction spans of 60, 180, and 360 days, respectively. Conclusions: The results confirm that the ConvLSTM model significantly improves the medium- to long-term prediction accuracy of UT1-UTC. These findings are crucial for autonomous determination of EOP and real-time applications, as well as for precise satellite orbit determination and other related fields.