This letter proposes a deep-learning-based fingerprinting indoor positioning method by mitigating catastrophic forgetting during the deep transfer learning process. Recently, indoor positioning methods based on fingerprint have made great development. The deep transfer learning technique has been applied to transfer the positioning network among different scenarios. But the catastrophic forgetting is a big challenge during the supervised transfer learning, which results in poor performance of fine-tuned network in source scenario. In order to solve this issue, the proposed method improved the fine-tuning learning procedure according to the importance of network parameters. It can adaptly control the ratio of network parameters by adding regulaizer to loss function. Simulation results show that the proposed method can effectively improve the positioning accuracy in source scenario without reducing the positioning accuracy in target scenario, especially for the transfer learning in multiple scenarios.