  Faraday rotation contains information about the magnetic field structure
along the line of sight and is an important instrument in the study of cosmic
magnetism. Traditional Faraday spectrum deconvolution methods such as RMCLEAN
face challenges in resolving complex Faraday dispersion functions and handling
large datasets. We develop a deep learning deconvolution model to enhance the
accuracy and efficiency of extracting Faraday rotation measures from radio
astronomical data, specifically targeting data from the MeerKAT Galaxy Cluster
Legacy Survey (MGCLS). We use semi-supervised learning, where the model
simultaneously recreates the data and minimizes the difference between the
output and the true signal of synthetic data. Performance comparisons with
RMCLEAN were conducted on simulated as well as real data for the galaxy cluster
Abell 3376. Our semi-supervised model is able to recover the Faraday dispersion
with great accuracy, particularly for complex or high-RM signals, maintaining
sensitivity across a broad RM range. The computational efficiency of this
method is significantly improved over traditional methods. Applied to
observations of Abell 3376, we find detailed magnetic field structures in the
radio relics, and several AGN. We also apply our model to MeerKAT data of Abell
85, Abell 168, Abell 194, Abell 3186 and Abell 3667.
