Deep reinforcement learning has been widely applied in charging station recommendations in the internet of vehicles, but training separate neural networks for each region are often required by traditional methods, leading to increased computational load and data demands. Transfer learning accelerates the learning process for new tasks by leveraging knowledge from previous tasks, thus reducing redundant training. Therefore, a transfer reinforcement learning-based cross-domain charging station recommendation algorithm was proposed. An embedding encoder was introduced by this algorithm to align the system state and action space dimensions between the source and target domains, effectively solving the dimensionality discrepancy problem. Additionally, variational distributions were constructed based on mutual information to maximize the similarity between pre-aligned and post-aligned target domain states to ensure effective transfer. Compared to three typical charging station recommendation algorithms, in the low-dimensional to high-dimensional transfer, the average total charging time of the proposed algorithm was reduced by 57.6%, 59.3%, and 7.1%. In the high-dimensional to low-dimensional transfer, the reductions were 12.3%, 40.8%, and 4.7%, respectively. Simulation results demonstrate that the proposed algorithm exhibits strong transferability and significantly enhances the performance of cross-domain charging station recommendation systems
