When a carrier (such as drones, ships, and vehicles) moves in extreme environments, the visibility of satellites may be lost, leading to a temporary or prolonged loss of lock on Global Navigation Satellite System (GNSS) signals. In such scenarios, an integrated navigation system is forced to switch to a pure Inertial Navigation System (INS). However, prolonged reliance on inertial navigation alone results in the accumulation of errors and a rapid decline in navigation accuracy. To address the rapid decline in INS accuracy after GNSS signal loss, a fusion navigation technology of GNSS and INS assisted by Transformer networks is proposed. When the GNSS signal is locked, the Transformer network utilizes current INS information and GNSS incremental data (the change in GNSS position information between two adjacent time periods) to train a mapping relationship between the two. When the GNSS signal is lost, the Transformer network leverages the previously established mapping relationship to predict GNSS incremental information based on the current INS data, and then integrates the INS information with the predicted GNSS data for navigation. Simulation results demonstrate that the Transformer network-assisted GNSS/INS fusion navigation technology can provide stable and reliable navigation signals even under conditions of temporary or prolonged GNSS signal loss. Furthermore, the Transformer network-assisted fusion navigation method offers a reference for other network-assisted fusion implementations.