Abstract: Given the limited coverage of ground-based ionospheric observations, it is challenging to obtain the global distribution of total electron content (TEC) using only ground-based sites. Utilizing machine learning techniques, we have developed a method to reconstruct global TEC maps driven by Beidou geostationary orbit (GEO) TEC. First, hypothetical TEC observations were sampled from IGS TEC located in the GNSS stations and used to pre-train the global TEC machine learning model. This pre-trained model is capable of reconstructing a global ionospheric TEC map based on TEC information over a few stations. However, significant differences exist in the spatial structure of TEC between IGS TEC and Beidou GEO TEC. The pre-trained model directly driven by Beidou TEC observations showed a significant systematic bias in the generated global TEC maps. To correct the bias between the generated TEC map and actual TEC observations, the transfer learning technique was adopted to optimize the pre-trained machine learning model. Through fine-tuning the pre-trained model with historical Beidou observations, the global information from hypothetical observations was introduced into a Beidou-driven model for global TEC reconstruction. The reconstruction accuracy of global ionospheric TEC maps driven by multi-site Beidou TEC observations has significantly improved with the optimization of transfer learning.