Encrypted video identification is an urgent problem in the field of network security and network management. The existing methods are to match the video transmission fingerprint of encrypted video with the video fingerprint in the video fingerprint database. The existing research mainly focuses on the study of matching recognition algorithm, but there is neither particular research on matching data sources nor the analysis of precision and false positive rate in large-scale video fingerprint library. The resulting practicality of existing methods cannot be guaranteed. In order to address this problem, this study firstly analyses the reason why the length of the cipher text of the application data unit (ADU) encrypted by TLS drifts relative to the length of the plaintext. For the first time, HTTP head feature and TLS fragment features are used as fitting features for ADU length restoration, and then this study proposes an accurate fingerprint restoration method HHTF for the encrypted ADU, and applies HHTF to the encrypted video recognition. A large fingerprint database of 200 000 videos was built based on the simulation of real Facebook videos. Theoretical derivation and calculation demonstrate that the accuracy, precision, and recall rate can reach 100%, and the false positive rate is 0 requiring only one-tenth the numbers of ADUs of the existing method. The experimental results in simulating large-scale video fingerprint database are consistent with the theoretical calculations. The application of the HHTF method makes it possible to recognize encrypted transmitted video in large-scale video fingerprint library scenarios, which is of great practicality and application value.