Transfer learning models have additional constraints on the properties of the molecules generated during training compared to pre-trained models. Extending the fine-tuning epoch of the model may result in the generation of molecules that are closer to those in the fine-tuned dataset, but it may also result in a decrease in the diversity and uniqueness of the molecules generated. Therefore, we chose to perform 45 epochs of fine-tuning as the loss values converged, and used the molecules generated in the last 5 epochs for subsequent parameter analyses. To optimize the training results, we will also tune and analyze the following three parameters: argument, dropout and temperature.