The Tibetan Plateau vortex (TPV) is a shallow mesoscale vortex system in the Tibetan Plateau’s main body. It occurs regularly, affects a wide area, and causes strong disasters. It is a major disaster-causing mesoscale system in China. To fully show the statistical characteristics of TPVs, a crucial basis for TPV research must be established. The accurate identification of TPVs is the key to the statistical characteristics of TPVs. TPV research has a better data basis with the emergence of reanalysis data with a high spatial and temporal resolution. However, neither an artificial identification approach nor an objective identification algorithm based on a coarser resolution can be effectively used for the current new reanalysis data. In this study, a restricted vorticity-based TPV identifying algorithm is proposed, which is suitable for high-resolution reanalysis data. This approach first determines the TPV candidate points, divides several octants with the candidate points as the center, and determines the center of the TPV by restricting the conditions of the average wind field in the octant and counterclockwise rotation (Northern Hemisphere) conditions of the octant group. This method can quickly identify the horizontal and vertical tracing of vortexes without complicated calculations and different thresholds for each pressure layer. A large sample evaluation of 15,466 TPVs (99,090 hours in total) in 42 warm seasons (May–September) from 1979 to 2020 shows that the average hit ratio of RTIA exceeds 95%, the average false alarm ratio is below 9%, and the average missing report rate is below 5%. Thus, the RTIA can correctly identify the centers of TPVs. Furthermore, the test results show that the high accuracy of TPV identification can still be maintained when RTIA is applied to the reanalysis data with different spatial resolutions (e.g., 0.5°or 0.25°). The identification results are primarily affected by the strength of the vortexes themselves, and the identification accuracy of weak vortexes is lower than that of strong vortexes. This approach can be used as a reference for identifying other mesoscale vortexes.