With the advent of the 5G era, there exist a large number of Internet of Things (IoT) terminals in the open campus network such as industrial area and campus network. Due to the huge data flow of IoT terminals, the problem of counterfeiting IoT terminals for network attack becomes increasingly serious, and the cost of computing resources of the existing IoT terminals identification technologies in the face of massive data increases gradually. To solve these problems, we propose a real-time IoT terminals identification algorithm for large-scale flow based on the time-sharing index of files. Firstly, the metadata for the time-sharing index of memory is established. Secondly, the time-sharing index of files is used to store the intermediate data of the construction session. Thirdly, the metadata trigger for the time-sharing index of memory is controlled to extract features from a small number of files and perform IoT terminals identification. In the experiment, on the premise of maintaining the accuracy of the IoT terminals identification algorithm, only a little disk space is occupied and the memory consumption is reduced by 92%. These results show that the proposed algorithm can be used in the framework of real-time IoT terminals identification.